Differential brain network analysis

ABSTRACT

A system and method of generating a graphical representation of a network of a subject human brain. The method comprises receiving, via a user interface, a selection of the network of the subject brain; determining, based on an MRI image of the subject brain and one or more identifiers associated with the selection, one or more parcellations of the subject brain (405); determining, using three-dimensional coordinates associated with each parcellation, corresponding tracts in a diffusion tensor image of the brain (425); and generating a graphical representation of the selected network (430), the graphical representation including at least one of (i) one or more surfaces representing the one or more parcellations, each surface generated using the coordinates, and (ii) the determined tracts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/066,178, filed Oct. 8, 2020, which application claims priority toAustralian Provisional Patent Application No. 2019903933 entitled“System and Method for Displaying a Network of a Brain,” listing MichaelSughrue and Stephane Doyen as inventors and filed Oct. 18, 2019, thecontents of which are incorporated herein in their entirety.

This application is related to U.S. patent application Ser. No.17/066,171 entitled “Processing of Brain Image Data to Assign Voxels toParcellations” listing Stephane Doyen, Charles Teo and Michael Sughrueas inventors, filed on Oct. 8, 2020, and incorporated by referenceherein in its entirety.

FIELD

The present invention relates generally to reproducing or displayingimages of a human brain, and in particular to displaying a graphicalrepresentation of a network of a human brain including structural andfunctional connections of the network based on captured image data. Thepresent invention also relates to a system, method and apparatus fordisplaying a graphical representation of a network of a human brain, andto a computer program product including a computer readable mediumhaving recorded thereon a computer program for displaying a graphicalrepresentation of a network of a human brain.

BACKGROUND

Diffusion tensor imaging (DTI) uses magnetic resonance images to measurediffusion of water in a human brain. The measured diffusion is used togenerate images of neural tracts and corresponding white matter fibersof the subject brain. Images captured using DTI relate to the wholebrain and are correspondingly complex.

Neurosurgeons typically view visual representations of DTI images for aparticular purpose, for example to study operation of a certain regionof the brain, study effects of certain conditions on the brain or toplan for surgery.

A region of the brain can include millions of fibers gathered as tracts.However, users (such as neurosurgeons) typically require greatergranularity in terms of operation and connections of the brain, such asidentifying which tracts or fibers are connected or related. Withoutaccess to improved granularity, a neurosurgeon's study of the brain canbe complex and may lead to risk in terms of identifying: 1) one or moreof conditions present in the brain; 2) relevant areas for surgery; and3) interactions between different components of the brain.

SUMMARY OF INVENTION

It is an object of the present invention to substantially overcome, orat least ameliorate, one or more disadvantages of existing arrangements.

According to one aspect of the present invention there is provided amethod of generating a graphical representation of a network of asubject human brain, including: receiving, via a user interface, aselection of the network of the subject brain; determining, based on anMRI image of the subject brain and one or more identifiers associatedwith the selection, one or more parcellations of the subject brain;determining, using three-dimensional coordinates associated with eachparcellation, corresponding tracts in a diffusion tensor image of thebrain; and generating a graphical representation of the selectednetwork, the graphical representation including at least one of (i) oneor more surfaces representing the one or more parcellations, eachsurface generated using the coordinates, and (ii) the determined tracts.A network can be interconnections of particular tracts and fiberscorresponding to a particular function or structure of the brain (suchas language or hearing).

According to another aspect of the present invention there is provided asystem, including: an image capture device configured to capture an MRIimage and a diffusion tensor image of a subject human brain; a memory;and a processor, wherein the processor is configured to execute codestored on the memory for implementing a method of generating a graphicalrepresentation of a network of the subject human brain, the methodincluding: receiving, via a user interface, a selection of the networkof the subject brain; determining, based on the MRI image of the subjectbrain and one or more identifiers associated with the selection, one ormore parcellations of the subject brain; determining, usingthree-dimensional coordinates associated with each parcellation,corresponding tracts in the diffusion tensor image of the brain; andgenerating a graphical representation of the selected network, thegraphical representation including at least one of (i) one or moresurfaces representing the one or more parcellations, each surfacegenerated using the coordinates, and (ii) the determined tracts.

According to another aspect of the present invention there is provided anon-transitory computer readable medium having a computer program storedthereon to implement a method of generating a graphical representationof a network of a subject human brain, the program including: code forreceiving, via a user interface, a selection of the network of thesubject brain; code for determining, based on an MRI image of thesubject brain and one or more identifiers associated with the selection,one or more parcellations of the subject brain; code for determining,using three-dimensional coordinates associated with each parcellation,corresponding tracts in a diffusion tensor image of the brain; and codefor generating a graphical representation of the selected network, thegraphical representation including at least one of (i) one or moresurfaces representing the one or more parcellations, each surfacegenerated using the coordinates, and (ii) the determined tracts.

According to another aspect of the present invention there is providedan apparatus configured to implement a method of generating a graphicalrepresentation of a network of a subject human brain, including: amemory; and a processor, wherein the processor is configured to executecode stored on the memory for: receiving, via a user interface, aselection of the network of the subject brain; determining, based on anMRI image of the subject brain and one or more identifiers associatedwith the selection, one or more parcellations of the subject brain;determining, using three-dimensional coordinates associated with eachparcellation, corresponding tracts in a diffusion tensor image of thebrain; and generating a graphical representation of the selectednetwork, the graphical representation including at least one of (i) oneor more surfaces representing the one or more parcellations, eachsurface generated using the coordinates, and (ii) the determined tracts.

The subject matter described in this specification can be implemented inparticular embodiments to realize one or more of the followingadvantages. Current interfaces can be of limited clinical assistance inthat such interfaces display too many tracts to be useful. Users of theinterfaces such as neurosurgeons face difficulty in determining whichtracts are connected and relevant to particular functions. Accordingly,particular tracts cannot be identified based on structure or functionand the image of the region of interest may not be clinicallymeaningful. Quality of patient care and complexity of diagnosis andsurgery can be adversely affected. Allowing a user to specify andvisualize particular functions and/or structures of interest, 1)improves quality and speed of care, 2) improves surgical planning as thesystem highlights important/relevant networks, and 3) allows for finerdetermination of head trauma based on a scan as the system displayspotentially impacted networks.

Other aspects are described below.

BRIEF DESCRIPTION OF DRAWINGS

At least one embodiment of the present invention will now be describedwith reference to the drawings and Table 2 at the end of thespecification, in which:

FIGS. 1A and 1B form a schematic block diagram of a computer system uponwhich arrangements described can be practiced;

FIG. 2A shows a software architecture for a graphical user interface forreproducing a graphical representation of specified network(s) of abrain;

FIG. 2B shows a data structure used in a database of FIG. 2A;

FIG. 3 shows a method of displaying a graphical representation of anetwork of a subject brain;

FIG. 4 shows a method of generating a graphical representation of anetwork of the subject brain as implemented in the method of FIG. 3;

FIG. 5 shows method of identifying tracts as used in the method of FIG.4;

FIGS. 6A and 6B show dataflows of generating a graphical representationof a network of a subject human brain;

FIG. 7A shows a window of a graphical user interface showing image dataof a subject brain;

FIG. 7B shows the window of 7A showing a graphical representation of aselected network (e.g., the language network) of the subject brain;

FIG. 8 shows a window of a graphical user interface showing image dataof a subject brain and an updated menu;

FIG. 9A shows a window of a graphical user interface showing a graphicalrepresentation of a network of a brain;

FIG. 9B shows the window of FIG. 9A updated to show a graphicalrepresentation of the network of a brain relating to tracts only;

FIG. 10 shows another window of a graphical user interface showing agraphical representation of a network of a brain;

FIG. 11 shows another window of a graphical user interface showing agraphical representation of a network of a brain; and

Table 2 at the end of the specification shows a mapping database usingthe structure of FIG. 2B.

DESCRIPTION OF EMBODIMENTS

Where reference is made in any one or more of the accompanying drawingsto steps and/or features, which have the same reference numerals, thosesteps and/or features have for the purposes of this description the samefunction(s) or operation(s), unless the contrary intention appears.

A brain atlas is a method of representing portions of the human brain. Abrain atlas typically comprises sections along anatomical or functionalareas of a brain and provides a mapping of the brain. One can refer tothe identified sections of the brain as parcellations of the brain. Forexample, one can delineate 180 areas/parcellations per hemisphere wherethe areas/parcellations are bounded by sharp changes in corticalarchitecture, function, connectivity, and/or topography. Suchparcellations can be determined based on a precisely aligned group(e.g., more than 200) healthy young adults.

The arrangements described allow a user of a medical image displaysystem, such as a neurosurgeon, to view DTI image data in a manner thatjust shows specified network(s) or interconnections of particular tractsand fibers corresponding to a particular function or structure of thebrain. A graphical representation that identifies particularparcellations and corresponding tracts, or portions of tracts, relevantto the structure can be provided. A network of the brain can beconstructed based upon parcellations of the brain and correspondingstructural and functional connections.

The arrangements described allow use of DTI images for a subject to beprovided in an improved manner so that a user can identify individualtracts or fibers relevant to interconnected or inter-operationalportions of the brain. For example, tracts (or fibers) associated withparticular parcellations or other known anatomical structures of thebrain and the spatial relationships of the tracts (or fibers) with theparcellation can be represented graphically. Compared to previoussolutions where all tracts in a region would be represented, therebyoccluding relationships between tracts (or fibers) with one another andwith certain portions of the brain, the user/viewer obtains a greatergranularity in relation to the image data and a more clinicallymeaningful image. A neurosurgeon, for example, is thereby allowed animproved study of a subject brain, for example interconnections ofparticular tracts, regions, and networks. Given the more clinicallymeaningful image, the neurosurgeon can better understand connections andoperations of the subject brain. Decisions relating to conditions,operation of the subject brain and procedures to be performed on thesubject brain can be improved, thereby increasing patient safety andstandard of care.

In order to allow a representation of the image data that isolates andidentifies interconnections associated with a grouping, function orregion of the brain, this specification provides a model mappingelements of the brain using atlas parcellations in accordance with athree-dimensional model of a brain. The model is effectively a libraryof neuro-anatomy that can be used to assign parcellations of the braininto networks for particular function(s). Implementations of a systemdescribed in this specification can use the structure of the model todetermine corresponding data from a DTI image and use that DTI data tographically represent a particular network of the brain. Such a librarystructure further allows a user such as a neurosurgeon to use thegraphical user interface accurately and intuitively to obtain a visualreconstruction of the brain of a particular subject to view networkinterconnections.

A computing device can perform the arrangements described. FIGS. 1A and1B depict a computer system 100, upon which one can practice the variousarrangements described.

As seen in FIG. 1A, the computer system 100 includes: a computer module101; input devices such as a keyboard 102, a mouse pointer device 103, ascanner 126, a camera 127, and a microphone 180; and output devicesincluding a printer 115, a display device 114 and loudspeakers 117. Anexternal Modulator-Demodulator (Modem) transceiver device 116 may beused by the computer module 101 for communicating to and from acommunications network 120 via a connection 121. The communicationsnetwork 120 may be a wide-area network (WAN), such as the Internet, acellular telecommunications network, or a private WAN. Where theconnection 121 is a telephone line, the modem 116 may be a traditional“dial-up” modem. Alternatively, where the connection 121 is a highcapacity (e.g., cable) connection, the modem 116 may be a broadbandmodem. A wireless modem may also be used for wireless connection to thecommunications network 120.

The computer module 101 typically includes at least one processor unit105, and a memory unit 106. For example, the memory unit 106 may havesemiconductor random access memory (RAM) and semiconductor read onlymemory (ROM). The computer module 101 also includes an number ofinput/output (I/O) interfaces including: an audio-video interface 107that couples to the video display 114, loudspeakers 117 and microphone180; an I/O interface 113 that couples to the keyboard 102, mouse 103,scanner 126, camera 127 and optionally a joystick or other humaninterface device (not illustrated); and an interface 108 for theexternal modem 116 and printer 115. In some implementations, the modem116 may be incorporated within the computer module 101, for examplewithin the interface 108. The computer module 101 also has a localnetwork interface 111, which permits coupling of the computer system 100via a connection 123 to a local-area communications network 122, knownas a Local Area Network (LAN). As illustrated in FIG. 1A, the localcommunications network 122 may also couple to the wide network 120 via aconnection 124, which would typically include a so-called “firewall”device or device of similar functionality. The local network interface111 may comprise an Ethernet circuit card, a Bluetooth® wirelessarrangement or an IEEE 802.11 wireless arrangement; however, numerousother types of interfaces may be practiced for the interface 111.

The module 101 can be connected with an image capture device 197 via thenetwork 120. The device 197 can capture images of a subject brain usingeach of diffusor tension imaging and magnetic resonance imaging (MRI)techniques. The captured images are typically in standard formats suchas DICOM format and OpenfMRI format respectively. The module 101 canreceive DTI and MRI images the device 197 via the network 120.Alternatively, the DTI and MRI images can be received by the module 101from a remote server, such as a cloud server 199, via the network 120.In other arrangements, the module 101 may be an integral part of one ofthe image capture device 197 and the server 199.

The I/O interfaces 108 and 113 may afford either or both of serial andparallel connectivity, the former typically being implemented accordingto the Universal Serial Bus (USB) standards and having corresponding USBconnectors (not illustrated). Storage devices 109 are provided andtypically include a hard disk drive (HDD) 110. Other storage devicessuch as a floppy disk drive and a magnetic tape drive (not illustrated)may also be used. An optical disk drive 112 is typically provided to actas a non-volatile source of data. Portable memory devices, such opticaldisks (e.g., CD-ROM, DVD, Blu ray Disc™), USB-RAM, portable, externalhard drives, and floppy disks, for example, may be used as appropriatesources of data to the system 100.

The components 105 to 113 of the computer module 101 typicallycommunicate via an interconnected bus 104 and in a manner that resultsin a conventional mode of operation of the computer system 100 known tothose in the relevant art. For example, the processor 105 is coupled tothe system bus 104 using a connection 118. Likewise, the memory 106 andoptical disk drive 112 are coupled to the system bus 104 by connections119. Examples of computers on which the described arrangements can bepractised include IBM-PC's and compatibles, Sun Sparcstations, AppleMac™ or like computer systems.

The method described may be implemented using the computer system 100wherein the processes of FIGS. 3 to 5, to be described, may beimplemented as one or more software application programs 133 executablewithin the computer system 100. In particular, the steps of the methoddescribed are effected by instructions 131 (see FIG. 1B) in the software133 that are carried out within the computer system 100. The softwareinstructions 131 may be formed as one or more code modules, each forperforming one or more particular tasks. The software may also bedivided into two separate parts, in which a first part and thecorresponding code modules performs the described methods and a secondpart and the corresponding code modules manage a user interface betweenthe first part and the user.

The software may be stored in a computer readable medium, including thestorage devices described below, for example. The software is loadedinto the computer system 100 from the computer readable medium, and thenexecuted by the computer system 100. A computer readable medium havingsuch software or computer program recorded on the computer readablemedium is a computer program product. The use of the computer programproduct in the computer system 100 preferably effects an advantageousapparatus for providing a display of a neurological image.

The software 133 is typically stored in the HDD 110 or the memory 106.The software is loaded into the computer system 100 from a computerreadable medium, and executed by the computer system 100. Thus, forexample, the software 133 may be stored on an optically readable diskstorage medium (e.g., CD-ROM) 125 that is read by the optical disk drive112. A computer readable medium having such software or computer programrecorded on it is a computer program product. The use of the computerprogram product in the computer system 100 preferably effects anapparatus for providing a display of a neurological image.

In some instances, the application programs 133 may be supplied to theuser encoded on one or more CD-ROMs 125 and read via the correspondingdrive 112, or alternatively may be read by the user from the networks120 or 122. Still further, the software can also be loaded into thecomputer system 100 from other computer readable media. Computerreadable storage media refers to any non-transitory tangible storagemedium that provides recorded instructions and/or data to the computersystem 100 for execution and/or processing. Examples of such storagemedia include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray′ Disc, ahard disk drive, a ROM or integrated circuit, USB memory, amagneto-optical disk, or a computer readable card such as a PCMCIA cardand the like, whether or not such devices are internal or external ofthe computer module 101. Examples of transitory or non-tangible computerreadable transmission media that may also participate in the provisionof software, application programs, instructions and/or data to thecomputer module 101 include radio or infra-red transmission channels aswell as a network connection to another computer or networked device,and the Internet or Intranets including e-mail transmissions andinformation recorded on Websites and the like.

The second part of the application programs 133 and the correspondingcode modules mentioned above may be executed to implement one or moregraphical user interfaces (GUIs) to be rendered or otherwise representedupon the display 114. Through manipulation of typically the keyboard 102and the mouse 103, a user of the computer system 100 and the applicationmay manipulate the interface in a functionally adaptable manner toprovide controlling commands and/or input to the applications associatedwith the GUI(s). Other forms of functionally adaptable user interfacesmay also be implemented, such as an audio interface utilizing speechprompts output via the loudspeakers 117 and user voice commands inputvia the microphone 180.

FIG. 1B is a detailed schematic block diagram of the processor 105 and a“memory” 134. The memory 134 represents a logical aggregation of all thememory modules (including the HDD 109 and semiconductor memory 106) thatcan be accessed by the computer module 101 in FIG. 1A.

When the computer module 101 is initially powered up, a power-onself-test (POST) program 150 executes. The POST program 150 is typicallystored in a ROM 149 of the semiconductor memory 106 of FIG. 1A. Ahardware device such as the ROM 149 storing software is sometimesreferred to as firmware. The POST program 150 examines hardware withinthe computer module 101 to ensure proper functioning and typicallychecks the processor 105, the memory 134 (109, 106), and a basicinput-output systems software (BIOS) module 151, also typically storedin the ROM 149, for correct operation. Once the POST program 150 has runsuccessfully, the BIOS 151 activates the hard disk drive 110 of FIG. 1A.Activation of the hard disk drive 110 causes a bootstrap loader program152 that is resident on the hard disk drive 110 to execute via theprocessor 105. This loads an operating system 153 into the RAM memory106, upon which the operating system 153 commences operation. Theoperating system 153 is a system level application, executable by theprocessor 105, to fulfil various high level functions, includingprocessor management, memory management, device management, storagemanagement, software application interface, and generic user interface.

The operating system 153 manages the memory 134 (109, 106) to ensurethat each process or application running on the computer module 101 hassufficient memory in which to execute without colliding with memoryallocated to another process. Furthermore, the different types of memoryavailable in the system 100 of FIG. 1A must be used properly so thateach process can run effectively. Accordingly, the aggregated memory 134is not intended to illustrate how particular segments of memory areallocated (unless otherwise stated), but rather to provide a generalview of the memory accessible by the computer system 100 and how such isused.

As shown in FIG. 1B, the processor 105 includes a number of functionalmodules including a control unit 139, an arithmetic logic unit (ALU)140, and a local or internal memory 148, sometimes called a cachememory. The cache memory 148 typically includes a number of storageregisters 144-146 in a register section. One or more internal busses 141functionally interconnect these functional modules. The processor 105typically also has one or more interfaces 142 for communicating withexternal devices via the system bus 104, using a connection 118. Thememory 134 is coupled to the bus 104 using a connection 119.

The application program 133 includes a sequence of instructions 131 thatmay include conditional branch and loop instructions. The program 133may also include data 132 which is used in execution of the program 133.The instructions 131 and the data 132 are stored in memory locations128, 129, 130 and 135, 136, 137, respectively. Depending upon therelative size of the instructions 131 and the memory locations 128-130,a particular instruction may be stored in a single memory location asdepicted by the instruction shown in the memory location 130.Alternately, an instruction may be segmented into a number of parts eachof which is stored in a separate memory location, as depicted by theinstruction segments shown in the memory locations 128 and 129.

In general, the processor 105 is given a set of instructions which areexecuted therein. The processor 105 waits for a subsequent input, towhich the processor 105 reacts to by executing another set ofinstructions. Each input may be provided from one or more of a number ofsources, including data generated by one or more of the input devices102, 103, data received from an external source across one of thenetworks 120, 102, data retrieved from one of the storage devices 106,109 or data retrieved from a storage medium 125 inserted into thecorresponding reader 112, all depicted in FIG. 1A. The execution of aset of the instructions may in some cases result in output of data.Execution may also involve storing data or variables to the memory 134.

The described arrangements use input variables 154, which are stored inthe memory 134 in corresponding memory locations 155, 156, 157. Thedescribed arrangements produce output variables 161, which are stored inthe memory 134 in corresponding memory locations 162, 163, 164.Intermediate variables 158 may be stored in memory locations 159, 160,166 and 167.

Referring to the processor 105 of FIG. 1B, the registers 144, 145, 146,the arithmetic logic unit (ALU) 140, and the control unit 139 worktogether to perform sequences of micro-operations needed to perform“fetch, decode, and execute” cycles for every instruction in theinstruction set making up the program 133. Each fetch, decode, andexecute cycle comprises:

a fetch operation, which fetches or reads an instruction 131 from amemory location 128, 129, 130;

a decode operation in which the control unit 139 determines whichinstruction has been fetched; and

an execute operation in which the control unit 139 and/or the ALU 140execute the instruction.

Thereafter, a further fetch, decode, and execute cycle for the nextinstruction may be executed. Similarly, a store cycle may be performedby which the control unit 139 stores or writes a value to a memorylocation 132.

Each step or sub-process in the processes of FIGS. 3 to 5 is associatedwith one or more segments of the program 133 and is performed by theregister section 144, 145, 147, the ALU 140, and the control unit 139 inthe processor 105 working together to perform the fetch, decode, andexecute cycles for every instruction in the instruction set for thenoted segments of the program 133.

FIG. 2 shows a software architecture 200 for implementing a graphicaluser interface to reconstruct and display a network of a subject brain.The architecture 200 includes a surface mesh 202, a mesh model 204, amapping database 206 and an interface module 210, each of which istypically stored in the memory 106. The interface engine 210 typicallyforms one of the modules of the software application 133 executable onthe processor 105. A mesh is a term often used in 3D computer renderingto describe a file that contains a cloud of points in space or a formulathe rendering of which will create that cloud. In the present case, amesh can provide the coordinates at which the parcellations are drawn.There can exist two meshes, a surface mesh and a mesh model. The surfacemesh 202 can provide the color to display for each relevant parcellationand the mesh model 204 can provide the voxel identity for use with thedatabase of parcellations. Note that this is just one possibleimplementation of the rendering method.

The interface module 210 executes to generate or render a graphical userinterface displayed on the monitor 114 for example. The graphical userinterface includes a number of menu options and a graphicalrepresentation of a network of a brain or a captured image of the brain.The interface model typically forms one or more modules of theapplication 133. In some arrangements, the module 210 may be accessed ordistributed by an internet browser executing on the module 101.

The mesh model 204 represents a three-dimensional structure of a shapeof a brain of a subject. The mesh model 204 can be constructed using apoint-cloud or a mesh of three-dimensional objects such as voxels. Inthe example arrangements described herein, the mesh model comprisescubic objects each representing a voxel. Each of the cubic objects hasan associated location in three-dimensional space (x, y and zcoordinates) representing the brain. Each point or voxel in the meshmodel 204 has an associated mesh identifier.

The surface mesh 202 comprises a model of the subject brain in whichcolor (described as a set of RGB values) is applied to voxels togenerate a surface representing parcellations of the brain. Voxels canbe assigned to parcellations using one of a variety of methods. In otherwords, the mesh can be derived from a personalised atlas in oneimplementation. In other implementations, other atlases can be used. Forinstance, one can use a warped HCP with no correction using a machinelearning model. The parcellations represent regions of the brain. TheRGB values are preferably assigned to parcellations in the followingmanner. The mesh is a cloud of points in space. Those points have an RGBvalue that can be derived from a look up table. The surface model 202associates a set of RGB values with a coordinate of each voxel, the RGBvalues reflecting a parcellation of the subject brain. Alternatively,other methods of assigning color may be used. Both of the surface mesh202 and the mesh model 204 are generated for each subject brain. Thesurface mesh 202 and the mesh model 204 are generated using MRI data forthe image brain as described in relation to FIG. 3 below. A color schemeis not required as parcellations involved in homogeneous functions aredisplayed in shades of the same color. Using such a color scheme makesthe display easier for a user to digest.

The mapping database 206 stores the model or library used to classifyportions of the brain into parcellations. The parcellations relate to abrain atlas and can be assigned identifiers in a specific order, asdescribed in more detail hereafter. The structure of the mappingdatabase 206 allows the user to select required parcellations ornetworks of the brain for which a network is to be generated using agraphical user interface.

The interface module 210 executes to use the surface mesh 202, meshmodel 204, the mapping database 206, and image data (both DTI and MRI)to render and display or reproduce a graphical representation of anetwork of the brain based on a user selection.

FIG. 2B shows one example of a data structure of the mapping database206. As shown in FIG. 2B a data structure 250 has a highest level 252named “Grouping”. The Grouping represents an initial menu option intowhich a user can drill down, if desired, to identify a particularnetwork of the brain to be represented graphically. A next level of thestructure 250 after the Grouping is termed Level 1 marked as 254,followed by Level 2 (marked as 256), in turn followed by a ParcellationLevel marked as 258. The Parcellation Level 258 is also referred to as aparcellation name. Each Parcellation Level 258 is associated with aunique identifier 260, referred to as a parcellation identifier. Asdescribed in relation to FIG. 4 below, each parcellation name 258 isdetermined based on the mapping database 206 connectingthree-dimensional locations in the subject brain with a parcellationidentifier 260. Another implementation of the data structure of themapping database 206 can have more or fewer levels.

An example of a mapping database 206 providing a library of a brainusing the data structure 250 is shown in Table 2 at the end of thespecification. As shown in Table 2, a particular sub-level may not bepresent for some of the levels 252 and 254.

Table 1 below shows an example set of Grouping options.

TABLE 1 Grouping options Grouping Types Network Parcellation TractRegion

The naming of grouping types can take various forms. For example,instead of a “Network” grouping type one can have a “Network template”grouping type and/or instead of a “Tracts” grouping type, one can have a“Tractography Bundle” grouping type.

In the context of the arrangements described a graphical representationof a network of a brain relates to parcellations of the brain and/orassociated tracts. The graphical representation of the network of thebrain can relate to selection of any of the Groupings “Network”,“Parcellation”, “Tract” and “Region” and associated sub-levels.

FIG. 7A shows an example of a window 700 a rendered by execution of theinterface module 210 on the module 101. The window 700 a can bereproduced via the display 114 for example. The window 700 a includes apull-down menu 702. The options available in the pull-down menu 702reflect the Grouping options of Table 1 (Grouping 252 of the datastructure 250).

Each of the levels 254 and 256 represent a portion of the brain,sub-divided in a progression such that the parcellation level (name) 258represents a typical parcellation of the brain used in a brain atlas.The representations provided by the levels 254 and 256 depend on thecorresponding Grouping 252. For example, in Table 2 below some Groupingshave a left Level 1 category and a right level 1 category for each ofleft and right. As shown in Table 2, the same parcellation name 258 canbe applied to more than one identifier as a parcellation may be relevantto more than one location or region (for example parcellation name “8C”is applied to identifiers 73 and 273 relating to left and rightinstances of Level 1 (auditory function) respectively). One can designthe structure 250 to divide portions of the brain in a manner intuitiveto a neurosurgeon or another neuroscience professional. One can use thedata structure 250 to identify relevant areas of a subject brain and/orof a DTI image as described below.

Referring to FIG. 7A, the window 700 a includes a sub-menu 703 with aslider 704. The sub-menu 703 includes options 707 reflecting Level 1(254) associated with the Grouping “Network”. Each of the optionsreflecting Level 1 can be selected by a user or expanded into Level 2(256) if available or the next sub-level until the Parcellation Level258 is reached.

The Grouping “Network” can relate to a network based on particularfunction such as auditory or language. The data structure 250 can begenerated to reflect known scientific classifications of the humanbrain. The data structure 250 allows actual structure and/or function ofthe human brain to be programmatically extracted so that differentportions of a subject brain and their interconnects can be identifiedand represented graphically. In particular, breaking the Grouping 252into different levels that lead to parcellations allows structure and/orfunction to be extracted, e.g., on the basis of specified network(s).

The structure 250 shown in FIG. 2B and the example database shown inTable 2 reflect anatomical breakdown in a manner intuitive to typicalneurosurgeons to form a preferred implementation. However, the structure250 can be varied in other implementations, for example adding furtherlevels, merging the levels 254 and 256, or otherwise further subdividingthe Grouping 252.

The mapping database 206 and the mesh model 204 operate to associate theparcellation identifier 260 with a three-dimensional coordinate ((x, y,z) coordinates) in the subject brain. A relationship is establishedbetween the mesh identifiers of the mesh model 204 and the parcellationidentifiers 260. For example, each point or voxel of the mesh model 204can be associated with one of a sequential number of mesh identifiersrepresenting a rasterization of three-dimensional locations in thebrain. The mapping database 206 associates the parcellation identifier260 with the parcellation name 258 as shown in FIG. 2B. Accordingly, theparcellation name 258 can be in turn associated with three-dimensionalcoordinates in the three-dimensional mesh model. Additionally, RGBvalues, each having values of between 0 and 255 are associated with eachparcellation name and/or identifier using the same coordinate system. Anexample is shown in Table 2 where the mesh identifier is associated withthe parcellation.

TABLE 2 Mapping database and mesh relationships Mesh Mesh ParcellationParcellation Color (R, G, Surface mesh Identifier Coordinate IdentiferMID name (stored in B) (stored in 202 (MID MID (stored (x, y, z) (storedin 206) 206) 206) with RGB in 204) (Stored in applied to 204) voxel)

In one implementation, the data included in the surface mesh, the meshmodel and the mapping database can be as follows: 1) surface mesh—Meshcoordinate, mesh ID, color, parcellation name, voxel ID; 2) meshmodel—mesh ID, mesh coordinate, parcellation ID; and 3) mappingdatabase-grouping, level 1, level 2, parcellation name, parcellation ID.In a specific implementation, Table 2 reflects the mapping database. Themesh gives the parcellation id in space that the rendering engine caninterpret. Putting the mapping database and the mesh model together onecan obtain the surface mesh, i.e., parcellations colored in space. Witha different file system, the surface mesh and the mesh model can becollapsed in one object. The example arrangements described relate touse of three-dimensional models and coordinates. However, in instanceswhere a two-dimensional representation of portions of a brain may berequired, the implementation described can be applied similarly to usetwo-dimensional models and coordinates. For example, in somecircumstances a neurosurgeon may prefer to use a two-dimensional modelfor improved ease of perception and reference, whereas in othercircumstances (for example during surgery) three-dimensional model maybe more appropriate.

FIG. 3 shows a method 300 of displaying a graphical representation of anetwork of a brain. With reference to FIGS. 1A, 2A and 3, the method 300is executed by the interface engine 210 under execution of the processor105.

The method 300 starts at an accessing step 305. At step 305 a system(such as the system illustrated in FIG. 1A) accesses images of a subjectbrain. The images are DTI and MRI images captured of the same subjectbrain, for example the image capture device 197 can capture the imagesand transmit them to the module 101 via the network 120 or the imagesmay be already stored in memory 106.

The method 300 continues from step 305 to a model preparation step 307.The step 307 operates to use the accessed MRI image data to construct agreyscale brain image, the mesh model 204, and the surface mesh 202 andpopulate the mapping database 206 for the subject brain.

T1 data of the MRI image allows construction of a greyscale brain imageaccording to known techniques. The T1 data represents athree-dimensional model of the subject brain in which each voxel isassociated with a greyscale value.

The step 307 operates to generate the mesh model 204 based on the voxelpositions. Each voxel or point in the mesh model has a three-dimensionallocation in the image. The step 307 executes to assign a mesh identifierto each of the voxels. For example, the identifier can be based on arasterization of the T1 data of the MRI image.

Population of the database structure 206 is achieved by associatingvoxels of the three-dimensional image of the subject brain (availablevia T1 data of the MRI) with one of the parcellation identifiers 260.Each parcellation identifier 260 is assigned in a specific order toestablish a relationship with the mesh identifiers. In the arrangementsdescribed each parcellation identifier 260 in the database is assignedbased on values as the mesh identifier in the corresponding (same)location in the mesh model 204. In other words, a particularparcellation identifier can be assigned to various mesh identifiers insuch a way as to use sequential mesh identifiers for voxels belonging toa single parcellation. This approach leverages the principle of databasenormalisation where normal forms are stored separately to avoidredundancy and easy update. If the system stores coordinates and colorsin the same database, one would have to update the whole database assoon as one updates the coordinates (e.g., for a new brain). Similarlyif one updates the colors one would have to update all the scansprocessed to date. Stated differently, ID's are invariants that are usedto look up elements that can change. In other arrangements, theparcellation identifier and the mesh identifier may be associated usingother methods such as an algorithm or a look up table. The mesh surface202 and the mesh model 204 allow a parcellation name 258 to be matchedto a volume in space and the identifiers 260 to be populated.

The surface model 202 is also generated at step 307 based on voxelpositions determined from the MRI data. Each of the voxels is associatedwith a set of RGB values in the database 206 for the correspondingparcellation value. The RGB values can be stored as part of the mappingdatabase 206 or the surface mesh 202. The RGB values can be derived asdescribed above The association of the coordinates of the mesh and theparcellations, and thereby the RGB values are based upon a brain atlas.For example, a standard HCP-MMP atlas, after conversion to a volumetricformat such as NIFTI, can be loaded and fitted to the T1 data of thesubject brain using fitting mechanisms such as curve fitting techniques,least squares fitting techniques, or volumetric fitting.

With reference to FIG. 3, the method 300 continues from step 307 to adisplaying step 310. With reference to FIGS. 1A and 3, the step 310operates to reproduce a default display of the graphical user interface,for example in the monitor 114. The default reproduction can be thewindow 700 a of FIG. 7A, for example. The window 700 a includes an area710 in which a graphical representation of a brain, or a network of abrain, can be reproduced. In the example of FIG. 7A, a defaultrepresentation is reproduced, being DTI image data for the full subjectbrain superimposed over the greyscale T1 image. In the example of FIG.7A, 730 shows greyscale graphics relating to the MRI data and 735 showsgraphics relating to the tracts determined from the DTI image.

The window generated by the interface can include a set of sliders 720for adjusting graphical elements of the interface display such ascontrast. The user can manipulate inputs of the module 101 such as themouse 103 to adjust the sliders 720. The menu 720 also includes optionsrelating to display of tracts and parcellations. In the example of FIG.7A, check-boxes 740 and 750 allow display of tracts and parcellations tobe switched on or off respectively. In the example of FIG. 7A, tractsare turned on (740 checked) and parcellations are turned off (750unchecked).

In another implementation, the default display can relate to the surfacemesh 202. FIG. 11 shows a window 1100 reproduced by execution of theinterface module 210. The window 1100 shows an example default displayin which only parcellation surfaces 1125 are reproduced rather than thetracts described by the DTI image. The window 1100 relates to thecheck-box 740 being unchecked and the check-box 750 being checked (seeFIG. 7A).

Returning to FIG. 3, the method 300 continues from step 310 to areceiving step 315. The step 315 operates to receive a user input. Theinput is received due to a user manipulating an input of the module 101,for example the mouse 103, to interact with the graphical userinterface, for example the window 700.

On receiving the input at step 315 the method 300 continues undercontrol of the processor 105 to a check step 320. Step 320 executes tocheck if the interaction requires a menu update. A menu update may berequired if the user has selected a different option from the menu 703of FIG. 7A, or selects to expand a currently available menu option, forexample one of options 707 available next to the slider 704. The options707 represent options corresponding to Level 1 (254) of the datastructure 250, each of which can be selected by the user or expended.Each of the options at Level 1 can be selected or expanded to provide asub-menu corresponding to Level 2 (256) The Level 2 options can in turnbe selected or expanded to represent selectable sub-options representingcorresponding parcellation levels or names (258).

If step 320 determines that a menu update is required (“Y” at step 320),the method 300 continues to an updating step 325. The updating stepupdates the graphical user interface to display menu options availableas a result of the interaction. For example, FIG. 8 shows a window 800reproduced by the interface engine 210. A pull-down menu 802(corresponding to the menu 702) shows a Grouping selection of “Network”.In the window 800 a menu area 803 (similar to the area 703) has hadLevel 1 (254) and a corresponding instance of Level 2 (256) expanded. Inthe window 800 Level 1, Level 2 and Parcellation Level 258 options aredisplayed as 812, 814 and 816 respectively.

Returning to FIG. 3, if step 320 determines that a menu update is notrequired (“N” at step 320), the method 300 continues to a determiningstep 330. The step 330 is therefore executed if the user has made aselection that requires a network of a brain to be graphicallyrepresented. The step 330 operates to determine each parcellationidentifier (260) associated with the user selection. Step 330 canexecute to determine more than one parcellation identifier, depending onthe user selection. For example, the user has selected more than oneparcellation 258, each parcellation has an associated identifier 260.Alternatively, if the user has selected a Level 1 (254) or Level 2 (256)option, the network can contain more than one parcellation such thatmore than one parcellation identifier is inherently selected. Forexample, with reference to FIG. 7A if the user selects the “language”option from the options 707, the system selects all the pacellationsthat are part of the language function.

The method 300 continues under execution of the processor 105 from step330 to a generating step 335. The step 335 executes to generate agraphical representation of the selected network of the subject brain.Step 335 uses the parcellation identifiers (260) determined in step 330,the mesh model 204 and the surface mesh 202 generated at step 307, andthe image data accessed at step 305 to generate a graphicalrepresentation of the network of the subject brain selected by the user.Operation of step 335 is described in greater detail in relation to FIG.4.

FIG. 4 shows an example of a method 400 of generating a graphicalrepresentation of a brain as executed at step 335. The method 400 istypically implemented as one or more modules of the application 210stored in the memory 106 and controlled under execution of the processor105.

The method 400 receives the parcellation identifiers 260 determined atstep 330. The step 405 operates to select one of the receivedparcellation identifiers. The selection may be based on any suitablecriteria, for example location, numerical order or random. Aparcellation name 258 corresponding to the selected identifier isdetermined at step 405 using the mapping database 206. Linking theselection menu to the rendering uses ID's. For example, a user canselect a network name using a user interface. The system uses thenetwork name to identify parcellation IDs and the system uses theparcellation IDs to determine where the parcellations are in 3D space.Steps 330 and 405 operate to determine, based on the MRI image of thesubject brain and the identifiers associated with the user selection,one or more parcellations of the subject brain.

The method 400 continues under control of the processor 105 from step405 to a determining step 410. Step 410 operates to determinethree-dimensional coordinates for the parcellation name 258. Thethree-dimensional coordinates reflect a location or region inthree-dimensional space on the mesh model 204. The three-dimensionalcoordinates are determined by matching the parcellation name 258 and/orthe associated identifier(s) 260 with identifiers of the mesh model 204.The coordinates are those of the matched mesh identifiers. Inimplementations where the data structure 250 is varied, identifiers fromdifferent levels may be used. In other words, the left hand side menucan show different subsets such as “Networks” or “tracts.” Animplementation of the system enables updates over the parcellationdatabase. As a result, the left hand side menu can be updated. Since thesystem can use ID's, the matching with the mesh is preserved.

The method 400 continues from step 410 to a determining step 415. Thestep 415 operates to determine image data corresponding to the locationor region identified at step 410 from the DTI image accessed at step305. The DTI image data is typically in “.TRK” format in which tractsare represented as lists of tract vectors having three-dimensionallocations. The system can identify tract vectors corresponding to thelocation or region determined at step 410. In one arrangement, thecoordinates associated with the three-dimensional mesh model 204 have asame origin as the image data such that the same three-dimensionallocation can be used for each. In other arrangements, a translation ofvectors may be required to align origins of the image data and the meshmodel 204. Step 415 effectively determines all tracts relevant to theuser selection.

The method 400 continues from step 415 to a check step 420. As noted inrelation to step 330 the user's selection can result in more than oneidentifier being determined. Step 420 executes to check if image datahas been determined for all of the identifiers received as inputs to themethod 400. If image data has been determined for all receivedidentifiers (“Y” at step 420) the method 400 continues to an identifyingstep 425. If image data has not been determined for all receivedidentifiers (“N” at step 420) a next identifier is selected and themethod 400 returns to step 405. The system then repeats steps 405 to 415for the next selected identifier.

The step 425 executes to select tracts from the image data that arerelevant to the network indicated by the user input, effectivelyorganising the tracts determined in each iteration of step 415 intosubsets based on the user selection. The tracts can relate to fulltracts or subsets of tracts. The subsets of tracts can includeindividual fibers. The tract vectors comprise sequences of vectorspresent in the image data that in combination represent routing oftracts. The system selects the tracts based on intersection (alsoreferred to as collision) with one or more volumes bounded by theselected parcellations. The volumes are determined based on thecoordinates determined at step 410. Steps 415 and 425 relate todetermining corresponding tracts in a diffusion tensor image of thebrain. The determination is made using the coordinates determined atstep 410. Operation of the step 425 is described in further detail inrelation to FIG. 5.

The method 400 continues from step 425 to a rendering step 430. At step430 the interface module 210 executes to render a graphicalrepresentation of the brain network and reproduce the graphical displayfor the user (for example via the video display 114). The graphicalrepresentation includes at least one of (i) one or more surfaces, eachrepresenting a parcellation boundary, and (ii) the tracts selected atstep 425. The graphical representation can include a greyscale imageproviding a background reference. Whether the graphical representationrelates to both the parcellation surfaces and the selected tracts orjust one of the selected tracts and the parcellation surfaces alonedepends on the selection received from the user at step 315.

Step 430 operates to generate surfaces representing parcellationboundaries (if required) based on the coordinates determined at step 410and the RGB values of the database 206 associated with the correspondingparcellation name. Each required surface is generated using selection ofthe regions defined in the surface mesh 202. If the user selectionrequires the tracts to be included in the graphical representation, thetract vectors of the DT image are used to generate the correspondinggraphical representation.

In the arrangements described, the surface and/or selected tracts arerendered superimposed on the greyscale image corresponding to the T1data of the MRI image. For example, FIG. 7B shows a window 700 breproduced by execution of the module 210. The window 700 b reproduces agraphical representation of a network of a brain shown in FIG. 7A basedon user selection of a Grouping “Network” and Level 1 “Language”. InFIG. 7B, a graphical representation in the display area 710 includes agreyscale background 720 generated using the MRI image. In analternative arrangement, a template image could be used as thebackground 720.

A number of surfaces (selected based on the user's menu selection andgenerated using the mesh surface 202) representing parcellations areoverlaid on the greyscale image 720, such as a surface 725. The selectedtracts from the DTI image are overlaid on the parcellation surfaces andthe template, for example as indicated by 730 b in the window 700 b.

The step 430 can use known rendering methods, such as three.js or volumerendering using Visualization Toolkit (VTK), for example to render thetracts and the parcellation surface.

FIG. 5 shows a method 500 of selecting tracts as implemented at step 425of the method 400. The method 500 is typically implemented as one ormore modules of the application 210 stored in the memory 106 andcontrolled under execution of the processor 105.

The method 500 receives the coordinates determined in iterations of step410 and the image data vectors determined at step 415. The method 500starts at a determining step 505. Step 505 operates to determine aboundary for each parcellation indicated by the user selection receivedat step 315. The boundary is determined based on the surface mesh 202associated with the parcellation. A similar method is used in step 430to generate a surface for rendering, as described above.

The method 500 continues from step 505 to a determining step 510. Step510 executes to determine intersections, also referred to as collisions,between of the image data with the generated surface. The intersectionsare determined based on modelling of operation of the subject brain overtime using the DTI image data and known software such as TrackVis, DiPY(Diffusion MRI image package in Python) or Brainlab. One can storetracts and parcellations according to a different data model. Tracts canbe stored as list of vectors with xyz coordinates for each point of eachvector. One xyz coordinate can have multiple tracts. Parcellations canbe stored in a simple tensor as only 1 parcelation id can be found for agiven xyz coordinate. The “collision detection” or intersection canconsist of scanning the full tract file for parcellations overlappingwith tract specific xyz coordinates. The intersections determined atstep 510 are in addition those determined to have correspondingcoordinates at step 415.

The method 500 continues from step 510 to a check step 515. The step 515operates to determine if more than one parcellation has been selected,as determined based on the number of parcellation identifiers determinedat step 330. If only one parcellation has been selected (“N”) at step515, the method 500 continues to a selecting step 520. The step 520executes to select all tracts intersecting or colliding with theselected parcellation surfaces.

If more than one parcellation has been selected (“Y” at step 515) themethod 500 continues to a check step 525. Step 525 executes to check if“Intra” mode has been selected. Referring to FIG. 7, Intra mode is adisplay option that can be made by a user that is relevant when morethan one parcellation in included in the selected network of the brain.A selectable intra button is show as 750, shown as turned off in thewindow 700. Intra mode determines whether all tracts colliding withselected parcellations are shown in the graphical representation of thenetwork or only tracts beginning and ending in the selectedparcellations are shown.

If Intra mode is selected (“Y”) at step 525 the method 500 continues toa selecting step 530. Step 530 operates to select only tracts startingand ending in the selected parcellations.

If intra mode is off (“N” at step 525) the method 500 continues to aselecting step 535. Step 535 operates to select all tracts collidingwith the selected parcellations irrespective of where the tracts startor end. In each of steps 520 and 535 the selected or determined tractscomprise all tracts intersecting regions associated with theparcellations determined from the user selection.

The method 500 ends after execution of any of steps 520, 530 and 535.

In another example, FIG. 9A shows a window 900 a reproduced by executionof the module 210. The window 900 a reproduces a graphicalrepresentation of a network of a brain based on user selection of aGrouping “Tract” and Level 1 “ILF” with Intra mode on. In FIG. 9A, agraphical representation in a display area 910 includes a greyscalebackground 920 generated using the MRI image. In an alternativearrangement, a template image could be used as the background 920.

A number of surfaces (selected based on the user's menu selection andgenerated using the mesh surface 202) representing parcellations areoverlaid on the greyscale image 920, such as a surface 925. The selectedtracts from the DTI image are overlaid on the parcellation surfaces andthe template, for example indicated as 930 in the window 900 a. In FIG.9a both tracts and parcellations are shown, corresponding to checking ofboth 740 and 750 of FIG. 7A.

FIG. 9B shows a window 900 b reproduced by execution of the module 210.The window 900 a reproduces a graphical representation of a network of abrain based on user selection of a Grouping “Tract” and Level 1 “ILF”with Intra mode on. In FIG. 9B only tracts 930 b are shown over thegreyscale image due selection of user display options (not shown). Forexample, using the example menu 720 of FIG. 7A, 740 would be checked and750 unchecked.

FIG. 6A shows a dataflow 600 associated with operation of step 307 ofthe method 300. The dataflow receives inputs of an MRI image 605 (asaccessed at step 305), a brain atlas 620, an RGB distribution 635 and aninitial mapping database 640. The brain atlas 620 can be a standardHCP-MMP atlas or another type of brain atlas. The RGB distribution 635assigns RGB values to parcellations. The initial mapping database 640relates to the data structure 250 in which parcellation identifiers havenot yet been assigned for the subject brain.

The step 307 operates to use T1 data comprising three-dimensionalcoordinates 610 of the MRI image 610. The three-dimensional coordinates610 in association with T1 greyscale data provide a greyscale image ofthe subject brain 615. Step 307 uses the coordinates 610 to creates themesh model 204 comprising the coordinates 610 each having an associatedmesh identifier.

The coordinates 610, the RGB distribution 635 and the atlas 620 are usedto generate the surface mesh 202.

The dataflow 600 generates data 625, relating population of theidentifiers 260 of the mapping database 206. The data 625 is generatedbased on the coordinates 610, the initial database 640 and the meshmodel 204 such that the identifiers 260 correspond to identifiers in thesame three-dimensional location of the mesh model 204. The mesh surface202 and the mesh model 204 allow a parcellation name to be matched to avolume in space and the identifiers 260 to be populated accordingly.

FIG. 6B shows a dataflow 650 associated with generating a graphicalrepresentation in terms of operation of FIGS. 3-5. As shown in thedataflow 650 a mesh identifier 655 is obtained in execution of step 303.The identifier 655 is used with the mapping database 206 to determine aparcellation name 665 (corresponding to 258) at step 405. Coordinates670 for the identifier 655 are determined using the mesh model 204, theparcellation name 665 and the mapping database 206 at step 410.

The coordinates 670 and a DTI image 690 of the subject brain are used atstep 415 to determine tract data 675 based on corresponding coordinates.The tract data relates to tracts as described in tract vector ((x, y,z)) form. A tract file can be a list of vectors in which each of thepoints constituting a vector are referenced in xyz coordinates. Thisapproach can be used as a tract vector is not typically in a straightline. A subset of tracts 680 is determined in operation of the method500 using the image 690 and the tract data 675. As described in relationto the method 500, the subset 680 may include all of the tracts 675(steps 520 and 535) or tracts beginning and ending in selectedparcellations only (step 530).

Step 430 operates to render a graphical representation 685 representingthe user selection using the tract subsets 680, the greyscale image 615and the surface mesh 202. A typical scan of a human brain can produceabout 300,000 tracts. Each tract can have several hundreds of xyzcoordinates.

As shown in FIG. 7A, graphical representation of the whole DTI imageincludes a high level of data that cannot be easily understood ordivided into relevant portions. The graphical representation, althoughbased on an actual captured image of the subject brain, provides limitedclinical assistance to a viewer such as a neurosurgeon. However,generating a graphical representation of a network of the brain basedusing the arrangements described can result in images such as that shownin FIG. 7B. In FIG. 7B a neurosurgeon or other healthcare professionalcan identify relevant interconnections relating to language functions ofthe subject brain from the DTI image intuitively. The interconnectionsare relevant in terms of both structure and function of the selectednetwork of the subject brain. The representation can be clinically moremeaningful for the surgeon compared to a showing all tracts in a region,depending on the reason for analysis of the subject brain. Similarly,the examples of FIGS. 9A and 9B show networks relating to ILF only, andprovide a more clinically meaningful image than current techniques.

In a further example, FIG. 10 shows a window 1000 reproduced byexecution of the module 210. The window 1000 reproduces a graphicalrepresentation of a network of a brain based on user selection of aGrouping “Tract” and Level 1 “FAT” with Intra mode off. The graphicalrepresentation includes surfaces representing parcellations (for example1025) and tracts (1030) derived from a DTI image of the subject brainand determined to be related to structural parcellation FAT (frontalaslant tract).

The data structure 250 and use of parcellations allows a neurosurgeon orother neuroscience professional to select the relevant network of thebrain intuitively. Further, the structure 250, mesh 204 and look uptable 212 when used in combination allow the relevant portions of theDTI image to be determined for inclusion in the user-selected network.

The arrangements described are applicable to the medical image captureand data processing industries and particularly for the medicalindustries related to neurology and associated healthcare.

The foregoing describes only some embodiments of the present invention,and modifications and/or changes can be made thereto without departingfrom the scope and spirit of the invention, the embodiments beingillustrative and not restrictive.

In the context of this specification, the word “comprising” means“including principally but not necessarily solely” or “having” or“including”, and not “consisting only of”. Variations of the word“comprising”, such as “comprise” and “comprises” have correspondinglyvaried meanings.

TABLE 2 Example mapping database Grouping Level 1 Level 2 Level 3Parcellation ID Network Auditory Left Frontal 44 74 Network AuditoryLeft Frontal 8C 73 Network Auditory Left Frontal FOP4 108 NetworkAuditory Right Frontal 44 274 Network Auditory Right Frontal 8C 273Network Auditory Right Frontal FOP4 308 Network Auditory Left ParietalPFcm 105 Network Auditory Left Parietal PSL 25 Network Auditory RightParietal PFcm 305 Network Auditory Right Parietal PSL 225 NetworkAuditory Left SMA SCEF 43 Network Auditory Right SMA SCEF 243 NetworkAuditory Left Temporal A1 24 Network Auditory Left Temporal A4 175Network Auditory Left Temporal A5 125 Network Auditory Left TemporalLBelt 174 Network Auditory Left Temporal MBelt 173 Network Auditory LeftTemporal PB elt 124 Network Auditory Left Temporal RI 104 NetworkAuditory Left Temporal STSdp 129 Network Auditory Left Temporal TPOJ1139 Network Auditory Right Temporal A1 224 Network Auditory RightTemporal A4 375 Network Auditory Right Temporal A5 325 Network AuditoryRight Temporal LBelt 374 Network Auditory Right Temporal MBelt 373Network Auditory Right Temporal PBelt 324 Network Auditory RightTemporal RI 304 Network Auditory Right Temporal STSdp 329 NetworkAuditory Right Temporal TPOJ1 339 Network CEN Left Frontal 46 84 NetworkCEN Left Frontal 8Ad 68 Network CEN Left Frontal 8Av 67 Network CEN LeftFrontal a47r 77 Network CEN Left Frontal IFSa 82 Network CEN LeftFrontal IFSp 81 Network CEN Left Frontal p47r 171 Network CEN LeftFrontal p9-46v 83 Network CEN Right Frontal 46 284 Network CEN RightFrontal 8Ad 268 Network CEN Right Frontal 8Av 267 Network CEN RightFrontal a47r 277 Network CEN Right Frontal IFSa 282 Network CEN RightFrontal IFSp 281 Network CEN Right Frontal p47r 371 Network CEN RightFrontal p9-46v 283 Network CEN Left Parietal AIP 117 Network CEN LeftParietal PF 148 Network CEN Left Parietal PFcm 105 Network CEN LeftParietal PFm 149 Network CEN Left Parietal PFt 116 Network CEN LeftParietal PSL 25 Network CEN Right Parietal AIP 317 Network CEN RightParietal PF 348 Network CEN Right Parietal PFcm 305 Network CEN RightParietal PFm 349 Network CEN Right Parietal PFt 316 Network CEN RightParietal PSL 225 Network DAN Left Dorsal Premotor 6a 96 Network DANRight Dorsal Premotor 6a 296 Network DAN Left Frontal FEF 10 Network DANRight Frontal FEF 210 Network DAN Left Lateral Stream MST 2 Network DANLeft Lateral Stream MT 23 Network DAN Left Lateral Stream PH 138 NetworkDAN Left Lateral Stream V4t 156 Network DAN Right Lateral Stream MST 202Network DAN Right Lateral Stream MT 223 Network DAN Right Lateral StreamPH 338 Network DAN Right Lateral Stream V4t 356 Network DAN LeftParietal 7PC 47 Network DAN Left Parietal AIP 117 Network DAN LeftParietal LIPd 95 Network DAN Left Parietal LIPv 48 Network DAN LeftParietal VIP 49 Network DAN Right Parietal 7PC 247 Network DAN RightParietal AIP 317 Network DAN Right Parietal LIPd 295 Network DAN RightParietal LIPv 248 Network DAN Right Parietal VIP 249 Network DMN LeftACC l0r 65 Network DMN Left ACC a24 61 Network DMN Left ACC p32 64Network DMN Left ACC s32 165 Network DMN Right ACC 10r 265 Network DMNRight ACC a24 261 Network DMN Right ACC p32 264 Network DMN Right ACCs32 365 Network DMN Left Lateral Parietal IP1 145 Network DMN LeftLateral Parietal PGi 150 Network DMN Left Lateral Parietal PGs 151Network DMN Left Lateral Parietal TPOJ3 141 Network DMN Right LateralParietal IP1 345 Network DMN Right Lateral Parietal PGi 350 Network DMNRight Lateral Parietal PGs 351 Network DMN Right Lateral Parietal TPOJ3341 Network DMN Left PCC 31a 162 Network DMN Left PCC 31pd 161 NetworkDMN Left PCC 31pv 35 Network DMN Left PCC d23ab 34 Network DMN Left PCCRSC 14 Network DMN Left PCC v23ab 33 Network DMN Right PCC 31a 362Network DMN Right PCC 31pd 361 Network DMN Right PCC 3 1pv 235 NetworkDMN Right PCC d23ab 234 Network DMN Right PCC RSC 214 Network DMN RightPCC v23ab 233 Network Language Left Frontal 44 74 Network Language LeftFrontal 45 75 Network Language Left Frontal 471 76 Network Language LeftFrontal 55b 12 Network Language Left Frontal 8C 73 Network Language LeftFrontal IFJa 79 Network Language Left Parietal AIP 117 Network LanguageLeft Parietal PFm 149 Network Language Left SMA SCEF 43 Network LanguageLeft SMA SFL 26 Network Language Left Temporal PBelt 124 NetworkLanguage Left Temporal PHT 137 Network Language Left Temporal STSdp 129Network Language Left Temporal STSvp 130 Network Language Left TemporalTElp 133 Network Accessory Left STSda 128 Language Network AccessoryLeft STSva 176 Language Network Accessory Left TE1a 132 Language NetworkAccessory Left TGv 172 Language Network Medial temporal Left BilateralEC 118 Network Medial temporal Left Bilateral PeEc 122 Network Medialtemporal Left Bilateral PHAl 126 Network Medial temporal Left BilateralPHA2 155 Network Medial temporal Left Bilateral PHA3 127 Network Medialtemporal Left Bilateral PreS 119 Network Medial temporal Right BilateralEC 318 Network Medial temporal Right Bilateral PeEc 322 Network Medialtemporal Right Bilateral PHAl 326 Network Medial temporal RightBilateral PHA2 355 Network Medial temporal Right Bilateral PHA3 327Network Medial temporal Right Bilateral PreS 319 Network Medial temporalLeft Subcortical Amygdala 418 Network Medial temporal Left SubcorticalHippocampus 417 Network Medial temporal Right Subcortical Amygdala 454Network Medial temporal Right Subcortical Hippocampus 453 NetworkNeglect Right Frontal 46 284 Network Neglect Right Frontal FEF 210Network Neglect Right Frontal p9-46v 283 Network Neglect Right ParietalAIP 317 Network Neglect Right Parietal PF 348 Network Neglect RightParietal PFcm 305 Network Neglect Right Parietal PFt 316 Network NeglectRight Primary 4 208 Network Neglect Right Primary 3a 253 Network NeglectRight Temporal A4 375 Network Neglect Right Temporal LBelt 374 NetworkNeglect Right Temporal PBelt 324 Network Neglect Right Temporal STSdp329 Network Praxis Left Frontal FOP4 108 Network Praxis Left LateralParietal PGi 150 Network Praxis Left Parietal 52 103 Network Praxis LeftParietal 7AL 42 Network Praxis Left Parietal 7PC 47 Network Praxis LeftParietal AIP 117 Network Praxis Left Parietal MIP 50 Network Praxis LeftParietal PFop 147 Network Praxis Left Primary 4 8 Network Praxis LeftPrimary 3b 9 Network Praxis Left SMA SCEF 43 Network Praxis LeftTemporal RI 104 Network Salience Left Cingulate a24pr 59 NetworkSalience Left Cingulate p32pr 60 Network Salience Right Cingulate a24pr259 Network Salience Right Cingulate p32pr 260 Network Salience LeftInsula AVI 111 Network Salience Left Insula FOP5 169 Network SalienceLeft Insula MI 109 Network Salience Right Insula AVI 311 NetworkSalience Right Insula FOP5 369 Network Salience Right Insula MI 309Network Sensorimotor Left Cingulate motor 24dd 40 Network SensorimotorLeft Cingulate motor 24dv 41 Network Sensorimotor Right Cingulate motor24dd 240 Network Sensorimotor Right Cingulate motor 24dv 241 NetworkSensorimotor Left Dorsal Premotor 6a 96 Network Sensorimotor Left DorsalPremotor 6d 54 Network Sensorimotor Right Dorsal Premotor 6a 296 NetworkSensorimotor Right Dorsal Premotor 6d 254 Network Sensorimotor LeftPrimary 1 51 Network Sensorimotor Left Primary 2 52 Network SensorimotorLeft Primary 4 8 Network Sensorimotor Left Primary 3a 53 NetworkSensorimotor Left Primary 3b 9 Network Sensorimotor Right Primary 1 251Network Sensorimotor Right Primary 2 252 Network Sensorimotor RightPrimary 4 208 Network Sensorimotor Right Primary 3a 253 NetworkSensorimotor Right Primary 3b 209 Network Sensorimotor Left SMA 6ma 44Network Sensorimotor Left SMA 6mp 55 Network Sensorimotor Left SMA SCEF43 Network Sensorimotor Left SMA SFL 26 Network Sensorimotor Right SMA6ma 244 Network Sensorimotor Right SMA 6mp 255 Network SensorimotorRight SMA SCEF 243 Network Sensorimotor Right SMA SFL 226 NetworkSensorimotor Left Ventral Premotor 6r 78 Network Sensorimotor LeftVentral Premotor 6v 56 Network Sensorimotor Right Ventral Premotor 6r278 Network Sensorimotor Right Ventral Premotor 6v 256 NetworkSubcortical Left Subcortical Accumbens 426 Network Subcortical LeftSubcortical Caudate 411 Network Subcortical Left Subcortical Cerebellum408 Network Subcortical Left Subcortical Pallidum 413 NetworkSubcortical Left Subcortical Putamen 412 Network Subcortical LeftSubcortical Thalamus 410 Network Subcortical Left Subcortical VentralDC428 Network Subcortical Right Subcortical Accumbens 458 NetworkSubcortical Right Subcortical Caudate 450 Network Subcortical RightSubcortical Cerebellum 447 Network Subcortical Right SubcorticalPallidum 452 Network Subcortical Right Subcortical Putamen 451 NetworkSubcortical Right Subcortical Thalamus 449 Network Subcortical RightSubcortical VentralDC 460 Network VAN Left Dorsal Premotor 6a 96 NetworkVAN Right Dorsal Premotor 6a 296 Network VAN Left Frontal 8C 73 NetworkVAN Left Frontal p9-46v 83 Network VAN Right Frontal 8C 273 Network VANRight Frontal p9-46v 283 Network VAN Left Inferior Parietal TPOJ2 140Network VAN Left Lateral Parietal PGi 150 Network VAN Right LateralParietal PGi 350 Network VAN Left Medial Parietal PCV 27 Network VANLeft Parietal MIP 50 Network VAN Left Parietal PFm 149 Network VAN RightParietal 7Am 245 Network VAN Right Parietal 7Pm 229 Network VAN RightParietal MIP 250 Network VAN Right Parietal PCV 227 Network VAN RightParietal PFm 349 Network VAN Right Parietal TPOJ2 340 Network VAN LeftSuperior Parietal 7Am 45 Network VAN Left Superior Parietal 7Pm 29Network VAN Left Ventral Premotor 6r 78 Network VAN Right VentralPremotor 6r 278 Network Visual Left Dorsal Stream IPS1 17 Network VisualLeft Dorsal Stream V3A 13 Network Visual Left Dorsal Stream V3B 19Network Visual Left Dorsal Stream V6 3 Network Visual Left Dorsal StreamV6A 152 Network Visual Left Dorsal Stream V7 16 Network Visual RightDorsal Stream IPS1 217 Network Visual Right Dorsal Stream V3A 213Network Visual Right Dorsal Stream V3B 219 Network Visual Right DorsalStream V6 203 Network Visual Right Dorsal Stream V6A 352 Network VisualRight Dorsal Stream V7 216 Network Visual Left Lateral Stream FST 157Network Visual Left Lateral Stream LO1 20 Network Visual Left LateralStream LO2 21 Network Visual Left Lateral Stream LO3 159 Network VisualLeft Lateral Stream MST 2 Network Visual Left Lateral Stream MT 23Network Visual Left Lateral Stream PH 138 Network Visual Left LateralStream V3CD 158 Network Visual Left Lateral Stream V4t 156 NetworkVisual Right Lateral Stream FST 357 Network Visual Right Lateral StreamLO1 220 Network Visual Right Lateral Stream LO2 221 Network Visual RightLateral Stream LO3 359 Network Visual Right Lateral Stream MST 202Network Visual Right Lateral Stream MT 223 Network Visual Right LateralStream PH 338 Network Visual Right Lateral Stream V3CD 358 NetworkVisual Right Lateral Stream V4t 356 Network Visual Left Medial V1 1Network Visual Left Medial V2 4 Network Visual Left Medial V3 5 NetworkVisual Left Medial V4 6 Network Visual Right Medial V1 201 NetworkVisual Right Medial V2 204 Network Visual Right Medial V3 205 NetworkVisual Right Medial V4 206 Network Visual Left Ventral Stream FFC 18Network Visual Left Ventral Stream PIT 22 Network Visual Left VentralStream V8 7 Network Visual Left Ventral Stream VMV1 153 Network VisualLeft Ventral Stream VMV2 160 Network Visual Left Ventral Stream VMV3 154Network Visual Left Ventral Stream VVC 163 Network Visual Right VentralStream FFC 218 Network Visual Right Ventral Stream PIT 222 NetworkVisual Right Ventral Stream V8 207 Network Visual Right Ventral StreamVMV1 353 Network Visual Right Ventral Stream VMV2 360 Network VisualRight Ventral Stream VMV3 354 Network Visual Right Ventral Stream VVC363 Parcellation Left Subcortical Accumbens 426 Parcellation LeftSubcortical Amygdala 418 Parcellation Left Subcortical Caudate 411Parcellation Left Subcortical Cerebellum 408 Parcellation LeftSubcortical Hippocampus 417 Parcellation Left Subcortical Pallidum 413Parcellation Left Subcortical Putamen 412 Parcellation Left SubcorticalThalamus 410 Parcellation Left Subcortical VentralDC 428 ParcellationRight Subcortical Accumbens 458 Parcellation Right Subcortical Amygdala454 Parcellation Right Subcortical Caudate 450 Parcellation RightSubcortical Cerebellum 447 Parcellation Right Subcortical Hippocampus453 Parcellation Right Subcortical Pallidum 452 Parcellation RightSubcortical Putamen 451 Parcellation Right Subcortical Thalamus 449Parcellation Right Subcortical VentralDC 460 Parcellation SubcorticalBrain-Stem 416 Parcellation Left 1 51 Parcellation Left 2 52Parcellation Left 4 8 Parcellation Left 25 164 Parcellation Left 43 99Parcellation Left 44 74 Parcellation Left 45 75 Parcellation Left 46 84Parcellation Left 52 103 Parcellation Left 10d 72 Parcellation Left 10pp90 Parcellation Left 10r 65 Parcellation Left 10v 88 Parcellation Left11I 91 Parcellation Left 13I 92 Parcellation Left 23c 38 ParcellationLeft 23d 32 Parcellation Left 24dd 40 Parcellation Left 24dv 41Parcellation Left 31a 162 Parcellation Left 31pd 161 Parcellation Left31pv 35 Parcellation Left 33pr 58 Parcellation Left 3a 53 ParcellationLeft 3b 9 Parcellation Left 47l 76 Parcellation Left 47m 66 ParcellationLeft 47s 94 Parcellation Left 55b 12 Parcellation Left 5L 39Parcellation Left 5m 36 Parcellation Left 5mv 37 Parcellation Left 6a 96Parcellation Left 6d 54 Parcellation Left 6ma 44 Parcellation Left 6mp55 Parcellation Left 6r 78 Parcellation Left 6v 56 Parcellation Left 7AL42 Parcellation Left 7Am 45 Parcellation Left 7m 30 Parcellation Left7PC 47 Parcellation Left 7PL 46 Parcellation Left 7Pm 29 ParcellationLeft 8Ad 68 Parcellation Left 8Av 67 Parcellation Left 8BL 70Parcellation Left 8BM 63 Parcellation Left 8C 73 Parcellation Left 9-46d86 Parcellation Left 9a 87 Parcellation Left 9m 69 Parcellation Left 9p71 Parcellation Left A1 24 Parcellation Left a10p 89 Parcellation Lefta24 61 Parcellation Left a24pr 59 Parcellation Left a32pr 179Parcellation Left A4 175 Parcellation Left a47r 77 Parcellation Left A5125 Parcellation Left a9-46v 85 Parcellation Left AAIC 112 ParcellationLeft AIP 117 Parcellation Left AVI 111 Parcellation Left d23ab 34Parcellation Left d32 62 Parcellation Left DVT 142 Parcellation Left EC118 Parcellation Left FEF 10 Parcellation Left FFC 18 Parcellation LeftFOP1 113 Parcellation Left FOP2 115 Parcellation Left FOP3 114Parcellation Left FOP4 108 Parcellation Left FOPS 169 Parcellation LeftFST 157 Parcellation Left H 120 Parcellation Left i6-8 97 ParcellationLeft IFJa 79 Parcellation Left IFJp 80 Parcellation Left IFSa 82Parcellation Left IFSp 81 Parcellation Left Ig 168 Parcellation Left IP0146 Parcellation Left IP1 145 Parcellation Left IP2 144 ParcellationLeft IPS1 17 Parcellation Left LBelt 174 Parcellation Left LIPd 95Parcellation Left LIPv 48 Parcellation Left LO1 20 Parcellation Left LO221 Parcellation Left LO3 159 Parcellation Left MBelt 173 ParcellationLeft MI 109 Parcellation Left MIP 50 Parcellation Left MST 2Parcellation Left MT 23 Parcellation Left OFC 93 Parcellation Left OP1101 Parcellation Left OP2-3 102 Parcellation Left OP4 100 ParcellationLeft p10p 170 Parcellation Left p24 180 Parcellation Left p24pr 57Parcellation Left p32 64 Parcellation Left p32pr 60 Parcellation Leftp47r 171 Parcellation Left p9-46v 83 Parcellation Left PBelt 124Parcellation Left PCV 27 Parcellation Left PeEc 122 Parcellation LeftPEF 11 Parcellation Left PF 148 Parcellation Left PFcm 105 ParcellationLeft PFm 149 Parcellation Left PFop 147 Parcellation Left PFt 116Parcellation Left PGi 150 Parcellation Left PGp 143 Parcellation LeftPGs 151 Parcellation Left PH 138 Parcellation Left PHA1 126 ParcellationLeft PHA2 155 Parcellation Left PHA3 127 Parcellation Left PHT 137Parcellation Left PI 178 Parcellation Left Pir 110 Parcellation Left PIT22 Parcellation Left pOFC 166 Parcellation Left PoI1 167 ParcellationLeft PoI2 106 Parcellation Left POS1 31 Parcellation Left POS2 15Parcellation Left PreS 119 Parcellation Left ProS 121 Parcellation LeftPSL 25 Parcellation Left RI 104 Parcellation Left RSC 14 ParcellationLeft s32 165 Parcellation Left s6-8 98 Parcellation Left SCEF 43Parcellation Left SFL 26 Parcellation Left STGa 123 Parcellation LeftSTSda 128 Parcellation Left STSdp 129 Parcellation Left STSva 176Parcellation Left STSvp 130 Parcellation Left STV 28 Parcellation LeftTA2 107 Parcellation Left TE1a 132 Parcellation Left TE1m 177Parcellation Left TE1p 133 Parcellation Left TE2a 134 Parcellation LeftTE2p 136 Parcellation Left TF 135 Parcellation Left TGd 131 ParcellationLeft TGv 172 Parcellation Left TPOJ1 139 Parcellation Left TPOJ2 140Parcellation Left TPOJ3 141 Parcellation Left V1 1 Parcellation Left V24 Parcellation Left v23ab 33 Parcellation Left V3 5 Parcellation LeftV3A 13 Parcellation Left V3B 19 Parcellation Left V3CD 158 ParcellationLeft V4 6 Parcellation Left V4t 156 Parcellation Left V6 3 ParcellationLeft V6A 152 Parcellation Left V7 16 Parcellation Left V8 7 ParcellationLeft VIP 49 Parcellation Left VMV1 153 Parcellation Left VMV2 160Parcellation Left VMV3 154 Parcellation Left VVC 163 Parcellation Right1 251 Parcellation Right 2 252 Parcellation Right 4 208 ParcellationRight 25 364 Parcellation Right 43 299 Parcellation Right 44 274Parcellation Right 45 275 Parcellation Right 46 284 Parcellation Right52 303 Parcellation Right 10d 272 Parcellation Right 10pp 290Parcellation Right 10r 265 Parcellation Right 10v 288 Parcellation Right11l 291 Parcellation Right 13l 292 Parcellation Right 23c 238Parcellation Right 23d 232 Parcellation Right 24dd 240 ParcellationRight 24dv 241 Parcellation Right 31a 362 Parcellation Right 31pd 361Parcellation Right 31pv 235 Parcellation Right 33pr 258 ParcellationRight 3a 253 Parcellation Right 3b 209 Parcellation Right 47l 276Parcellation Right 47m 266 Parcellation Right 47s 294 Parcellation Right55b 212 Parcellation Right 5L 239 Parcellation Right 5m 236 ParcellationRight 5mv 237 Parcellation Right 6a 296 Parcellation Right 6d 254Parcellation Right 6ma 244 Parcellation Right 6mp 255 Parcellation Right6r 278 Parcellation Right 6v 256 Parcellation Right 7AL 242 ParcellationRight 7Am 245 Parcellation Right 7m 230 Parcellation Right 7PC 247Parcellation Right 7PL 246 Parcellation Right 7Pm 229 Parcellation Right8Ad 268 Parcellation Right 8Av 267 Parcellation Right 8BL 270Parcellation Right 8BM 263 Parcellation Right 8C 273 Parcellation Right9-46d 286 Parcellation Right 9a 287 Parcellation Right 9m 269Parcellation Right 9p 271 Parcellation Right A1 224 Parcellation Righta10p 289 Parcellation Right a24 261 Parcellation Right a24pr 259Parcellation Right a32pr 379 Parcellation Right A4 375 ParcellationRight a47r 277 Parcellation Right A5 325 Parcellation Right a9-46v 285Parcellation Right AAIC 312 Parcellation Right AIP 317 ParcellationRight AVI 311 Parcellation Right d23ab 234 Parcellation Right d32 262Parcellation Right DVT 342 Parcellation Right EC 318 Parcellation RightFEF 210 Parcellation Right FFC 218 Parcellation Right FOP1 313Parcellation Right FOP2 315 Parcellation Right FOP3 314 ParcellationRight FOP4 308 Parcellation Right FOPS 369 Parcellation Right FST 357Parcellation Right H 320 Parcellation Right i6-8 297 Parcellation RightIFJa 279 Parcellation Right IFJp 280 Parcellation Right IFSa 282Parcellation Right IFSp 281 Parcellation Right Ig 368 Parcellation RightIP0 346 Parcellation Right IP1 345 Parcellation Right IP2 344Parcellation Right IPS1 217 Parcellation Right LBelt 374 ParcellationRight LIPd 295 Parcellation Right LIPv 248 Parcellation Right LO1 220Parcellation Right LO2 221 Parcellation Right LO3 359 Parcellation RightMBelt 373 Parcellation Right MI 309 Parcellation Right MIP 250Parcellation Right MST 202 Parcellation Right MT 223 Parcellation RightOFC 293 Parcellation Right OP1 301 Parcellation Right OP2-3 302Parcellation Right OP4 300 Parcellation Right p10p 370 ParcellationRight p24 380 Parcellation Right p24pr 257 Parcellation Right p32 264Parcellation Right p32pr 260 Parcellation Right p47r 371 ParcellationRight p9-46v 283 Parcellation Right PBelt 324 Parcellation Right PCV 227Parcellation Right PeEc 322 Parcellation Right PEF 211 ParcellationRight PF 348 Parcellation Right PFcm 305 Parcellation Right PFm 349Parcellation Right PFop 347 Parcellation Right PFt 316 ParcellationRight PGi 350 Parcellation Right PGp 343 Parcellation Right PGs 351Parcellation Right PH 338 Parcellation Right PHA1 326 Parcellation RightPHA2 355 Parcellation Right PHA3 327 Parcellation Right PHT 337Parcellation Right PI 378 Parcellation Right Pir 310 Parcellation RightPIT 222 Parcellation Right pOFC 366 Parcellation Right PoI1 367Parcellation Right PoI2 306 Parcellation Right POS1 231 ParcellationRight POS2 215 Parcellation Right Pre S 319 Parcellation Right ProS 321Parcellation Right PSL 225 Parcellation Right RI 304 Parcellation RightRSC 214 Parcellation Right s32 365 Parcellation Right s6-8 298Parcellation Right SCEF 243 Parcellation Right SFL 226 ParcellationRight STGa 323 Parcellation Right STSda 328 Parcellation Right STSdp 329Parcellation Right STSva 376 Parcellation Right STSvp 330 ParcellationRight STV 228 Parcellation Right TA2 307 Parcellation Right TE1a 332Parcellation Right TE1m 377 Parcellation Right TE1p 333 ParcellationRight TE2a 334 Parcellation Right TE2p 336 Parcellation Right TF 335Parcellation Right TGd 331 Parcellation Right TGv 372 Parcellation RightTPOJ1 339 Parcellation Right TPOJ2 340 Parcellation Right TPOJ3 341Parcellation Right V1 201 Parcellation Right V2 204 Parcellation Rightv23ab 233 Parcellation Right V3 205 Parcellation Right V3A 213Parcellation Right V3B 219 Parcellation Right V3CD 358 ParcellationRight V4 206 Parcellation Right V4t 356 Parcellation Right V6 203Parcellation Right V6A 352 Parcellation Right V7 216 Parcellation RightV8 207 Parcellation Right VIP 249 Parcellation Right VMV1 353Parcellation Right VMV2 360 Parcellation Right VMV3 354 ParcellationRight VVC 363 Tract Cingulum Left ACC 10r 65 Tract Cingulum Left ACC a2461 Tract Cingulum Left ACC p32 64 Tract Cingulum Left ACC s32 165 TractCingulum Right ACC 10r 265 Tract Cingulum Right ACC a24 261 TractCingulum Right ACC p32 264 Tract Cingulum Right ACC s32 365 TractCingulum Left Bilateral EC 118 Tract Cingulum Left Bilateral PeEc 122Tract Cingulum Left Bilateral PreS 119 Tract Cingulum Right Bilateral EC318 Tract Cingulum Right Bilateral PeEc 322 Tract Cingulum RightBilateral Pre S 319 Tract Cingulum Left Cingulate a24pr 59 TractCingulum Left Cingulate p32pr 60 Tract Cingulum Right Cingulate a24pr259 Tract Cingulum Right Cingulate p32pr 260 Tract Cingulum Left DorsalStream V6 3 Tract Cingulum Right Dorsal Stream V6 203 Tract CingulumLeft Frontopolar 10d 72 Tract Cingulum Right Frontopolar 10d 272 TractCingulum Left Medial V1 1 Tract Cingulum Left Medial V2 4 Tract CingulumRight Medial V1 201 Tract Cingulum Right Medial V2 204 Tract CingulumLeft Medial Frontal 25 164 Tract Cingulum Left Medial Frontal 33pr 58Tract Cingulum Left Medial Frontal 8BM 63 Tract Cingulum Left MedialFrontal 9m 69 Tract Cingulum Left Medial Frontal a32pr 179 TractCingulum Left Medial Frontal d32 62 Tract Cingulum Left Medial Frontalp24 180 Tract Cingulum Left Medial Frontal p24pr 57 Tract Cingulum RightMedial Frontal 25 364 Tract Cingulum Right Medial Frontal 33pr 258 TractCingulum Right Medial Frontal 8BM 263 Tract Cingulum Right MedialFrontal 9m 269 Tract Cingulum Right Medial Frontal a32pr 379 TractCingulum Right Medial Frontal d32 262 Tract Cingulum Right MedialFrontal p24 380 Tract Cingulum Right Medial Frontal p24pr 257 TractCingulum Left Medial Parietal 23c 38 Tract Cingulum Left Medial Parietal23d 32 Tract Cingulum Left Medial Parietal 7m 30 Tract Cingulum LeftMedial Parietal DVT 142 Tract Cingulum Left Medial Parietal PCV 27 TractCingulum Left Medial Parietal POS1 31 Tract Cingulum Left MedialParietal POS2 15 Tract Cingulum Left Medial Parietal ProS 121 TractCingulum Right Medial Parietal 23d 232 Tract Cingulum Right MedialParietal 7m 230 Tract Cingulum Right Medial Parietal DVT 342 TractCingulum Right Medial Parietal POS1 231 Tract Cingulum Right MedialParietal POS2 215 Tract Cingulum Right Medial Parietal ProS 321 TractCingulum Right Parietal PCV 227 Tract Cingulum Left PCC 31a 162 TractCingulum Left PCC 31pd 161 Tract Cingulum Left PCC 31pv 35 TractCingulum Left PCC d23ab 34 Tract Cingulum Left PCC RSC 14 Tract CingulumLeft PCC v23ab 33 Tract Cingulum Right PCC 31a 362 Tract Cingulum RightPCC 31pd 361 Tract Cingulum Right PCC 31pv 235 Tract Cingulum Right PCCd23ab 234 Tract Cingulum Right PCC RSC 214 Tract Cingulum Right PCCv23ab 233 Tract Cingulum Left SMA SCEF 43 Tract Cingulum Right SMA SCEF243 Tract Cingulum Right Superior Parietal 23c 238 Tract FAT Left DLPFCs6-8 98 Tract FAT Right DLPFC s6-8 298 Tract FAT Left Frontal 44 74Tract FAT Left Frontal FOP4 108 Tract FAT Right Frontal 44 274 Tract FATRight Frontal FOP4 308 Tract FAT Left Insula MI 109 Tract FAT RightInsula MI 309 Tract FAT Left Medial Frontal 8BL 70 Tract FAT RightMedial Frontal 8BL 270 Tract FAT Left SMA 6ma 44 Tract FAT Left SMA SFL26 Tract FAT Right SMA 6ma 244 Tract FAT Right SMA SFL 226 Tract FATLeft Superior FOP1 113 Opercula Tract FAT Left Superior FOP3 114Opercula Tract FAT Right Superior FOP1 313 Opercula Tract FAT RightSuperior FOP3 314 Opercula Tract FAT Left Ventral Premotor 6r 78 TractFAT Right Ventral Premotor 6r 278 Tract ILF Left Accessory TE1a 132Language Tract ILF Left Accessory TGv 172 Language Tract ILF LeftBilateral PeEc 122 Tract ILF Left Bilateral PHA2 155 Tract ILF LeftBilateral PHA3 127 Tract ILF Right Bilateral PeEc 322 Tract ILF RightBilateral PHA2 355 Tract ILF Right Bilateral PHA3 327 Tract ILF LeftDorsal Stream V3A 13 Tract ILF Left Dorsal Stream V3B 19 Tract ILF LeftDorsal Stream V6A 152 Tract ILF Left Dorsal Stream V7 16 Tract ILF RightDorsal Stream V3A 213 Tract ILF Right Dorsal Stream V3B 219 Tract ILFRight Dorsal Stream V6A 352 Tract ILF Right Dorsal Stream V7 216 TractILF Left Inferior Parietal PGp 143 Tract ILF Right Inferior Parietal PGp343 Tract ILF Left Lateral Parietal TPOJ3 141 Tract ILF Right LateralParietal TPOJ3 341 Tract ILF Left Lateral Stream LO3 159 Tract ILF LeftLateral Stream MST 2 Tract ILF Left Lateral Stream MT 23 Tract ILF LeftLateral Stream PH 138 Tract ILF Right Lateral Stream LO3 359 Tract ILFRight Lateral Stream MST 202 Tract ILF Right Lateral Stream MT 223 TractILF Right Lateral Stream PH 338 Tract ILF Left Medial V1 1 Tract ILFLeft Medial V2 4 Tract ILF Left Medial V3 5 Tract ILF Left Medial V4 6Tract ILF Right Medial V1 201 Tract ILF Right Medial V2 204 Tract ILFRight Medial V3 205 Tract ILF Right Medial V4 206 Tract ILF LeftParietal 52 103 Tract ILF Left Supramarginal PI 178 Gyrus Tract ILFRight Supramarginal 52 303 Gyrus Tract ILF Right Supramarginal PI 378Gyrus Tract ILF Left Temporal A5 125 Tract ILF Left Temporal MBelt 173Tract ILF Left Temporal STGa 123 Tract ILF Left Temporal TA2 107 TractILF Left Temporal TF 135 Tract ILF Left Temporal TGd 131 Tract ILF RightTemporal A5 325 Tract ILF Right Temporal MBelt 373 Tract ILF RightTemporal STGa 323 Tract ILF Right Temporal TA2 307 Tract ILF RightTemporal TE1a 332 Tract ILF Right Temporal TF 335 Tract ILF RightTemporal TGd 331 Tract ILF Right Temporal TGv 372 Tract ILF Left VentralStream FFC 18 Tract ILF Left Ventral Stream V8 7 Tract ILF Left VentralStream VMV1 153 Tract ILF Left Ventral Stream VMV2 160 Tract ILF LeftVentral Stream VMV3 154 Tract ILF Left Ventral Stream VVC 163 Tract ILFRight Ventral Stream FFC 218 Tract ILF Right Ventral Stream V8 207 TractILF Right Ventral Stream VMV1 353 Tract ILF Right Ventral Stream VMV2360 Tract ILF Right Ventral Stream VMV3 354 Tract ILF Right VentralStream VVC 363 Tract IFOF Left DLPFC 9a 87 Tract IFOF Left DLPFC 9p 71Tract IFOF Right DLPFC 47l 276 Tract IFOF Right DLPFC 9a 287 Tract IFOFRight DLPFC 9p 271 Tract IFOF Left Dorsal Premotor 6a 96 Tract IFOFRight Dorsal Premotor 6a 296 Tract IFOF Left Dorsal Stream IPS1 17 TractIFOF Left Dorsal Stream V3A 13 Tract IFOF Left Dorsal Stream V6 3 TractIFOF Left Dorsal Stream V6A 152 Tract IFOF Left Dorsal Stream V7 16Tract IFOF Right Dorsal Stream IPS1 217 Tract IFOF Right Dorsal StreamV3A 213 Tract IFOF Right Dorsal Stream V6 203 Tract IFOF Right DorsalStream V6A 352 Tract IFOF Right Dorsal Stream V7 216 Tract IFOF LeftFrontal 45 75 Tract IFOF Left Frontal 47l 76 Tract IFOF Left Frontala47r 77 Tract IFOF Right Frontal a47r 277 Tract IFOF Left Frontopolar10d 72 Tract IFOF Left Frontopolar 10pp 90 Tract IFOF Left Frontopolara10p 89 Tract IFOF Left Frontopolar p10p 170 Tract IFOF RightFrontopolar 10d 272 Tract IFOF Right Frontopolar 10pp 290 Tract IFOFRight Frontopolar a10p 289 Tract IFOF Right Frontopolar p10p 370 TractIFOF Left Insula FOP5 169 Tract IFOF Right Insula FOP5 369 Tract IFOFLeft Medial V1 1 Tract IFOF Left Medial V2 4 Tract IFOF Left Medial V3 5Tract IFOF Left Medial V4 6 Tract IFOF Right Medial V1 201 Tract IFOFRight Medial V2 204 Tract IFOF Right Medial V3 205 Tract IFOF RightMedial V4 206 Tract IFOF Left Medial Frontal 8BL 70 Tract IFOF LeftMedial Frontal 9m 69 Tract IFOF Right Medial Frontal 8BL 270 Tract IFOFRight Medial Frontal 9m 269 Tract IFOF Left Orbitofrontal 11l 91 TractIFOF Left Orbitofrontal 47s 94 Tract IFOF Left Orbitofrontal OFC 93Tract IFOF Right Orbitofrontal 11l 291 Tract IFOF Right Orbitofrontal47s 294 Tract IFOF Right Orbitofrontal OFC 293 Tract IFOF Left Parietal7AL 42 Tract IFOF Left Parietal 7PC 47 Tract IFOF Left Parietal MIP 50Tract IFOF Right Parietal 7Am 245 Tract IFOF Right Parietal 7PC 247Tract IFOF Right Parietal MIP 250 Tract IFOF Left SMA 6ma 44 Tract IFOFLeft SMA SFL 26 Tract IFOF Right SMA 6ma 244 Tract IFOF Right SMA SFL226 Tract IFOF Left Superior Parietal 7Am 45 Tract IFOF Left SuperiorParietal 7PL 46 Tract IFOF Right Superior Parietal 7AL 242 Tract IFOFRight Superior Parietal 7PL 246 Tract IFOF Left Ventral Stream VMV2 160Tract IFOF Right Ventral Stream VMV2 360 Tract IFOF Right 45 275 TractMdLF Left Accessory STSda 128 Language Tract MdLF Left Accessory STSva176 Language Tract MdLF Left Accessory TE1a 132 Language Tract MdLF LeftDorsal Stream IPS1 17 Tract MdLF Left Dorsal Stream V3A 13 Tract MdLFLeft Dorsal Stream V3B 19 Tract MdLF Left Dorsal Stream V6 3 Tract MdLFLeft Dorsal Stream V6A 152 Tract MdLF Left Dorsal Stream V7 16 TractMdLF Right Dorsal Stream IPS1 217 Tract MdLF Right Dorsal Stream V3A 213Tract MdLF Right Dorsal Stream V3B 219 Tract MdLF Right Dorsal Stream V6203 Tract MdLF Right Dorsal Stream V6A 352 Tract MdLF Right DorsalStream V7 216 Tract MdLF Left Insula Proper PoI1 167 Tract MdLF LeftInsula Proper PoI2 106 Tract MdLF Right Insula Proper PoI1 367 TractMdLF Right Insula Proper PoI2 306 Tract MdLF Left IPS IP0 146 Tract MdLFRight IPS IP0 346 Tract MdLF Left Lateral Parietal IP1 145 Tract MdLFRight Lateral Parietal IP1 345 Tract MdLF Left Lateral Stream V3CD 158Tract MdLF Right Lateral Stream V3CD 358 Tract MdLF Left Medial V1 1Tract MdLF Left Medial V2 4 Tract MdLF Left Medial V3 5 Tract MdLF LeftMedial V4 6 Tract MdLF Right Medial V1 201 Tract MdLF Right Medial V2204 Tract MdLF Right Medial V3 205 Tract MdLF Right Medial V4 206 TractMdLF Left Parietal LIPd 95 Tract MdLF Left Parietal LIPv 48 Tract MdLFLeft Parietal MIP 50 Tract MdLF Left Parietal VIP 49 Tract MdLF RightParietal LIPd 295 Tract MdLF Right Parietal LIPv 248 Tract MdLF RightParietal MIP 250 Tract MdLF Right Parietal VIP 249 Tract MdLF LeftSuperior Parietal 7PL 46 Tract MdLF Right Superior Parietal 7PL 246Tract MdLF Left Supramarginal PI 178 Gyrus Tract MdLF RightSupramarginal PI 378 Gyrus Tract MdLF Left Temporal A1 24 Tract MdLFLeft Temporal A4 175 Tract MdLF Left Temporal A5 125 Tract MdLF LeftTemporal MBelt 173 Tract MdLF Left Temporal PBelt 124 Tract MdLF LeftTemporal STGa 123 Tract MdLF Left Temporal STSdp 129 Tract MdLF LeftTemporal TGd 131 Tract MdLF Right Temporal A1 224 Tract MdLF RightTemporal A4 375 Tract MdLF Right Temporal A5 325 Tract MdLF RightTemporal MBelt 373 Tract MdLF Right Temporal PBelt 324 Tract MdLF RightTemporal STGa 323 Tract MdLF Right Temporal STSda 328 Tract MdLF RightTemporal STSdp 329 Tract MdLF Right Temporal STSva 376 Tract MdLF RightTemporal TE1a 332 Tract MdLF Right Temporal TGd 331 Tract SLF/AF LeftAccessory STSda 128 Language Tract SLF/AF Left Accessory STSva 176Language Tract SLF/AF Left Accessory TE1a 132 Language Tract SLF/AF LeftDLPFC IFJp 80 Tract SLF/AF Left DLPFC PEF 11 Tract SLF/AF Right DLPFCIFJa 279 Tract SLF/AF Right DLPFC IFJp 280 Tract SLF/AF Right DLPFC PEF211 Tract SLF/AF Left Dorsal Premotor 6a 96 Tract SLF/AF Right DorsalPremotor 6a 296 Tract SLF/AF Left Frontal 44 74 Tract SLF/AF LeftFrontal 45 75 Tract SLF/AF Left Frontal 46 84 Tract SLF/AF Left Frontal55b 12 Tract SLF/AF Left Frontal 8Av 67 Tract SLF/AF Left Frontal 8C 73Tract SLF/AF Left Frontal FEF 10 Tract SLF/AF Left Frontal FOP4 108Tract SLF/AF Left Frontal IFJa 79 Tract SLF/AF Left Frontal IFSa 82Tract SLF/AF Left Frontal IFSp 81 Tract SLF/AF Left Frontal p9-46v 83Tract SLF/AF Right Frontal 44 274 Tract SLF/AF Right Frontal 46 284Tract SLF/AF Right Frontal 8Av 267 Tract SLF/AF Right Frontal 8C 273Tract SLF/AF Right Frontal FEF 210 Tract SLF/AF Right Frontal FOP4 308Tract SLF/AF Right Frontal IFSa 282 Tract SLF/AF Right Frontal IFSp 281Tract SLF/AF Right Frontal p9-46v 283 Tract SLF/AF Left InferiorParietal TPOJ2 140 Tract SLF/AF Left Insula FOP5 169 Tract SLF/AF LeftInsula MI 109 Tract SLF/AF Right Insula FOP5 369 Tract SLF/AF RightInsula MI 309 Tract SLF/AF Left IPS IP2 144 Tract SLF/AF Right IPS IP2344 Tract SLF/AF Left Lateral Parietal IP1 145 Tract SLF/AF Left LateralParietal PGs 151 Tract SLF/AF Right Lateral Parietal IP1 345 TractSLF/AF Right Lateral Parietal PGs 351 Tract SLF/AF Left Lateral StreamFST 157 Tract SLF/AF Left Lateral Stream PH 138 Tract SLF/AF RightLateral Stream FST 357 Tract SLF/AF Right Lateral Stream PH 338 TractSLF/AF Left Medial Frontal 8BM 63 Tract SLF/AF Right Medial Frontal 8BM263 Tract SLF/AF Left Parietal 7PC 47 Tract SLF/AF Left Parietal AIP 117Tract SLF/AF Left Parietal LIPd 95 Tract SLF/AF Left Parietal MIP 50Tract SLF/AF Left Parietal PF 148 Tract SLF/AF Left Parietal PFcm 105Tract SLF/AF Left Parietal PFm 149 Tract SLF/AF Left Parietal PFt 116Tract SLF/AF Left Parietal PSL 25 Tract SLF/AF Right Parietal 7PC 247Tract SLF/AF Right Parietal AIP 317 Tract SLF/AF Right Parietal LIPd 295Tract SLF/AF Right Parietal MIP 250 Tract SLF/AF Right Parietal PF 348Tract SLF/AF Right Parietal PFcm 305 Tract SLF/AF Right Parietal PFm 349Tract SLF/AF Right Parietal PFt 316 Tract SLF/AF Right Parietal PSL 225Tract SLF/AF Right Parietal TPOJ2 340 Tract SLF/AF Left Primary 1 51Tract SLF/AF Left Primary 2 52 Tract SLF/AF Left Primary 4 8 TractSLF/AF Left Primary 3a 53 Tract SLF/AF Left Primary 3b 9 Tract SLF/AFRight Primary 1 251 Tract SLF/AF Right Primary 2 252 Tract SLF/AF RightPrimary 4 208 Tract SLF/AF Right Primary 3a 253 Tract SLF/AF RightPrimary 3b 209 Tract SLF/AF Left Superior 43 99 Opercula Tract SLF/AFLeft Superior FOP1 113 Opercula Tract SLF/AF Left Superior FOP2 115Opercula Tract SLF/AF Left Superior FOP3 114 Opercula Tract SLF/AF LeftSuperior OP4 100 Opercula Tract SLF/AF Right Superior 43 299 OperculaTract SLF/AF Right Superior FOP1 313 Opercula Tract SLF/AF RightSuperior FOP2 315 Opercula Tract SLF/AF Right Superior FOP3 314 OperculaTract SLF/AF Right Superior OP4 300 Opercula Tract SLF/AF LeftSupramarginal STV 28 Gyrus Tract SLF/AF Right Supramarginal STV 228Gyrus Tract SLF/AF Left Temporal A1 24 Tract SLF/AF Left Temporal A4 175Tract SLF/AF Left Temporal A5 125 Tract SLF/AF Left Temporal LBelt 174Tract SLF/AF Left Temporal PBelt 124 Tract SLF/AF Left Temporal PHT 137Tract SLF/AF Left Temporal RI 104 Tract SLF/AF Left Temporal STSdp 129Tract SLF/AF Left Temporal STSvp 130 Tract SLF/AF Left Temporal TE1m 177Tract SLF/AF Left Temporal TE1p 133 Tract SLF/AF Left Temporal TE2a 134Tract SLF/AF Left Temporal TE2p 136 Tract SLF/AF Left Temporal TF 135Tract SLF/AF Left Temporal TGd 131 Tract SLF/AF Left Temporal TPOJ1 139Tract SLF/AF Right Temporal A1 224 Tract SLF/AF Right Temporal A4 375Tract SLF/AF Right Temporal A5 325 Tract SLF/AF Right Temporal LBelt 374Tract SLF/AF Right Temporal PBelt 324 Tract SLF/AF Right Temporal PHT337 Tract SLF/AF Right Temporal RI 304 Tract SLF/AF Right Temporal STSda328 Tract SLF/AF Right Temporal STSdp 329 Tract SLF/AF Right TemporalSTSva 376 Tract SLF/AF Right Temporal STSvp 330 Tract SLF/AF RightTemporal TEla 332 Tract SLF/AF Right Temporal TElm 377 Tract SLF/AFRight Temporal TE1p 333 Tract SLF/AF Right Temporal TE2a 334 TractSLF/AF Right Temporal TE2p 336 Tract SLF/AF Right Temporal TF 335 TractSLF/AF Right Temporal TGd 331 Tract SLF/AF Right Temporal TPOJ1 339Tract SLF/AF Left Ventral Premotor 6r 78 Tract SLF/AF Left VentralPremotor 6v 56 Tract SLF/AF Right Ventral Premotor 6r 278 Tract SLF/AFRight Ventral Premotor 6v 256 Tract SLF/AF Right 45 275 Tract SLF/AFRight 55b 212 Tract UF Right DLPFC 471 276 Tract UF Left Frontal 44 74Tract UF Left Frontal 45 75 Tract UF Left Frontal 47l 76 Tract UF LeftFrontal FOP4 108 Tract UF Right Frontal 44 274 Tract UF Right FrontalFOP4 308 Tract UF Left Insula FOP5 169 Tract UF Right Insula FOP5 369Tract UF Left Orbitofrontal 47s 94 Tract UF Left Orbitofrontal OFC 93Tract UF Left Orbitofrontal pOFC 166 Tract UF Right Orbitofrontal 47s294 Tract UF Right Orbitofrontal OFC 293 Tract UF Right OrbitofrontalpOFC 366 Tract UF Left Temporal STGa 123 Tract UF Left Temporal TGd 131Tract UF Right Temporal STGa 323 Tract UF Right Temporal TGd 331 TractUF Right 45 275 Tract VOF Left Dorsal Stream V3A 13 Tract VOF LeftDorsal Stream V3B 19 Tract VOF Left Dorsal Stream V7 16 Tract VOF RightDorsal Stream V3A 213 Tract VOF Right Dorsal Stream V3B 219 Tract VOFRight Dorsal Stream V7 216 Tract VOF Left Lateral Stream V3CD 158 TractVOF Right Lateral Stream V3CD 358 Tract VOF Left Medial V2 4 Tract VOFLeft Medial V3 5 Tract VOF Right Medial V2 204 Tract VOF Right Medial V3205 Tract VOF Left Ventral Stream PIT 22 Tract VOF Left Ventral StreamV8 7 Tract VOF Left Ventral Stream VMV1 153 Tract VOF Left VentralStream VMV2 160 Tract VOF Left Ventral Stream VMV3 154 Tract VOF LeftVentral Stream VVC 163 Tract VOF Right Ventral Stream PIT 222 Tract VOFRight Ventral Stream V8 207 Tract VOF Right Ventral Stream VMV1 353Tract VOF Right Ventral Stream VMV2 360 Tract VOF Right Ventral StreamVMV3 354 Tract VOF Right Ventral Stream VVC 363 Region DLPFC Left 9-46d86 Region DLPFC Left 9a 87 Region DLPFC Left 9p 71 Region DLPFC Lefta9-46v 85 Region DLPFC Left i6-8 97 Region DLPFC Left IFJp 80 RegionDLPFC Left PEF 11 Region DLPFC Left s6-8 98 Region DLPFC Right 471 276Region DLPFC Right 9-46d 286 Region DLPFC Right 9a 287 Region DLPFCRight 9p 271 Region DLPFC Right a9-46v 285 Region DLPFC Right i6-8 297Region DLPFC Right IFJa 279 Region DLPFC Right IFJp 280 Region DLPFCRight PEF 211 Region DLPFC Right s6-8 298 Region Frontopolar Left 10d 72Region Frontopolar Left 10pp 90 Region Frontopolar Left a10p 89 RegionFrontopolar Left p10p 170 Region Frontopolar Right 10d 272 RegionFrontopolar Right 10pp 290 Region Frontopolar Right a10p 289 RegionFrontopolar Right p10p 370 Region Inferior Parietal Left PGp 143 RegionInferior Parietal Left TPOJ2 140 Region Inferior Parietal Right PGp 343Region Insula Proper Left AAIC 112 Region Insula Proper Left Ig 168Region Insula Proper Left Pir 110 Region Insula Proper Left Pol1 167Region Insula Proper Left PoI2 106 Region Insula Proper Right AAIC 312Region Insula Proper Right Ig 368 Region Insula Proper Right Pir 310Region Insula Proper Right PoI1 367 Region Insula Proper Right PoI2 306Region IPS Left IP0 146 Region IPS Left IP2 144 Region IPS Right IP0 346Region IPS Right IP2 344 Region Medial Frontal Left 25 164 Region MedialFrontal Left 10v 88 Region Medial Frontal Left 33pr 58 Region MedialFrontal Left 8BL 70 Region Medial Frontal Left 8BM 63 Region MedialFrontal Left 9m 69 Region Medial Frontal Left a32pr 179 Region MedialFrontal Left d32 62 Region Medial Frontal Left p24 180 Region MedialFrontal Left p24pr 57 Region Medial Frontal Right 25 364 Region MedialFrontal Right 10v 288 Region Medial Frontal Right 33pr 258 Region MedialFrontal Right 8BL 270 Region Medial Frontal Right 8BM 263 Region MedialFrontal Right 9m 269 Region Medial Frontal Right a32pr 379 Region MedialFrontal Right d32 262 Region Medial Frontal Right p24 380 Region MedialFrontal Right p24pr 257 Region Medial Parietal Left 23c 38 Region MedialParietal Left 23d 32 Region Medial Parietal Left 7m 30 Region MedialParietal Left DVT 142 Region Medial Parietal Left PCV 27 Region MedialParietal Left POS1 31 Region Medial Parietal Left POS2 15 Region MedialParietal Left ProS 121 Region Medial Parietal Right 23d 232 RegionMedial Parietal Right 7m 230 Region Medial Parietal Right DVT 342 RegionMedial Parietal Right POS1 231 Region Medial Parietal Right POS2 215Region Medial Parietal Right ProS 321 Region Othitofrontal Left 11l 91Region Othitofrontal Left 13l 92 Region Othitofrontal Left 47m 66 RegionOthitofrontal Left 47s 94 Region Othitofrontal Left OFC 93 RegionOthitofrontal Left p0FC 166 Region Othitofrontal Right 11l 291 RegionOthitofrontal Right 13l 292 Region Othitofrontal Right 47m 266 RegionOthitofrontal Right 47s 294 Region Orbitofrontal Right OFC 293 RegionOthitofrontal Right pOFC 366 Region Superior Left 43 99 Opercula RegionSuperior Left FOP1 113 Opercula Region Superior Left FOP2 115 OperculaRegion Superior Left FOP3 114 Opercula Region Superior Left OP1 101Opercula Region Superior Left OP2-3 102 Opercula Region Superior LeftOP4 100 Opercula Region Superior Right 43 299 Opercula Region SuperiorRight FOP1 313 Opercula Region Superior Right FOP2 315 Opercula RegionSuperior Right FOP3 314 Opercula Region Superior Right OP1 301 OperculaRegion Superior Right OP2-3 302 Opercula Region Superior Right OP4 300Opercula Region Superior Right PFop 347 Opercula Region SuperiorParietal Left 5L 39 Region Superior Parietal Left 5m 36 Region SuperiorParietal Left 5mv 37 Region Superior Parietal Left 7Am 45 RegionSuperior Parietal Left 7PL 46 Region Superior Parietal Left 7Pm 29Region Superior Parietal Right 23c 238 Region Superior Parietal Right 5L239 Region Superior Parietal Right 5m 236 Region Superior Parietal Right5mv 237 Region Superior Parietal Right 7AL 242 Region Superior ParietalRight 7PL 246 Region Supramarginal Left PI 178 Gyrus RegionSupramarginal Left STV 28 Gyrus Region Supramarginal Right 52 303 GyrusRegion Supramarginal Right PI 378 Gyrus Region Supramarginal Right STV228 Gyrus Region Temporal Left STGa 123 Region Temporal Left TA2 107Region Temporal Left TE1m 177 Region Temporal Left TE2a 134 RegionTemporal Left TE2p 136 Region Temporal Left TF 135 Region Temporal LeftTGd 131 Region Temporal Right PHT 337 Region Temporal Right STGa 323Region Temporal Right STSda 328 Region Temporal Right STSva 376 RegionTemporal Right STSvp 330 Region Temporal Right TA2 307 Region TemporalRight TE1a 332 Region Temporal Right TE1m 377 Region Temporal Right TE1p333 Region Temporal Right TE2a 334 Region Temporal Right TE2p 336 RegionTemporal Right TF 335 Region Temporal Right TGd 331 Region TemporalRight TGv 372

1. (canceled)
 2. A method comprising: receiving a selection of a networkof a subject brain to specify a selected network; determiningparcellations of the selected network to produce determinedparcellations; determining, using three-dimensional coordinatesassociated with each determined parcellation, corresponding tracts indiffusion tensor image data of the subject brain to produce determinedtracts, wherein determining corresponding tracts in a diffusion tensorimage of the subject brain to produce determined tracts comprises:receiving a tract selection mode indication to select tracts that: a)begin in one of the determined parcellations and b) end in another oneof the determined parcellations; and responsive to the tract selectionmode indication, determining corresponding tracts in a diffusion tensorimage of the brain to produce determined tracts that a) begin in one ofthe determined parcellations and b) end in another one of the determinedparcellations; and generating graphical representation data of theselected network, the graphical representation data including at leastone of (i) one or more surfaces representing the one or more determinedparcellations, each surface generated using the coordinates, and (ii)the determined tracts.
 3. The method according to claim 2, wherein eachof the determined parcellations is determined based on a databaseassociating three-dimensional locations in the subject brain with aparcellation identifier.
 4. The method according to claim 2, whereineach of the determined parcellations is determined based on aparcellation name and a corresponding region of the subject brain. 5.The method of claim 2, wherein the determined tracts comprise all tractsintersecting regions associated with the determined one or moreparcellations.
 6. The method of claim 2, wherein the determined tractscomprise all tracts that both begin and end in regions of a human brainoccupied by the determined parcellations.
 7. The method according toclaim 2, wherein determining the determined parcellations comprisesgenerating a three-dimensional model of the brain from an MM image, eachvoxel of the MM image having an associated identifier.
 8. The methodaccording to claim 7, further comprising generating a surface model, thesurface model associating a set of RGB values with a coordinate of eachvoxel, the RGB values reflecting a parcellation of the subject brain,and wherein the one or more surfaces are generated using the surfacemodel.
 9. The method according to claim 7, wherein a mapping databaseassociates the identifiers such that each of a plurality of parcellationidentifiers matches a mesh identifier in the correspondingthree-dimensional location.
 10. The method according to claim 2, whereinthe three-dimensional coordinates associated with each parcellation aredetermined based on voxel positions in MRI data.
 11. The methodaccording to claim 2, wherein the corresponding tracts include subsetsof tracts.
 12. A system comprising one or more computers and one or morestorage devices on which are stored instructions that are operable, whenexecuted by the one or more computers, to cause the one or morecomputers to perform operations comprising: receiving a selection of anetwork of a subject brain to specify a selected network; determiningparcellations of the selected network to produce determinedparcellations; determining, using three-dimensional coordinatesassociated with each determined parcellation, corresponding tracts indiffusion tensor image data of the subject brain to produce determinedtracts, wherein determining corresponding tracts in a diffusion tensorimage of the subject brain to produce determined tracts comprises:receiving a tract selection mode indication to select tracts that: a)begin in one of the determined parcellations and b) end in another oneof the determined parcellations; and responsive to the tract selectionmode indication, determining corresponding tracts in a diffusion tensorimage of the brain to produce determined tracts that a) begin in one ofthe determined parcellations and b) end in another one of the determinedparcellations; and generating graphical representation data of theselected network, the graphical representation data including at leastone of (i) one or more surfaces representing the one or more determinedparcellations, each surface generated using the coordinates, and (ii)the determined tracts.
 13. The system according to claim 12, whereineach of the determined parcellations is determined based on a databaseassociating three-dimensional locations in the subject brain with aparcellation identifier.
 14. The system according to claim 12, whereineach of the determined parcellations is determined based on aparcellation name and a corresponding region of the subject brain. 15.The system of claim 12, wherein the determined tracts comprise alltracts intersecting regions associated with the determinedparcellations.
 16. The system of claim 12, wherein the determined tractscomprise all tracts that both begin and end in regions of a human brainoccupied by the determined parcellations.
 17. The system according toclaim 12, wherein determining the determined parcellations comprisesgenerating a three-dimensional model of the brain from an MM image, eachvoxel of the MM image having an associated identifier.
 18. The systemaccording to claim 17, wherein the operations further comprisegenerating a surface model, the surface model associating a set of RGBvalues with a coordinate of each voxel, the RGB values reflecting aparcellation of the subject brain, and wherein the one or more surfacesare generated using the surface model.
 19. The system according to claim17, wherein a mapping database associates the identifiers such that eachof a plurality of parcellation identifiers matches a mesh identifier inthe corresponding three-dimensional location.
 20. The system accordingto claim 12, wherein the three-dimensional coordinates associated witheach parcellation are determined based on voxel positions in Mill data.21. The system according to claim 12, wherein the corresponding tractsinclude subsets of tracts.